Group decision making in health care : A case study of multidisciplinary meetings
- Sharma, Vishakha, Stranieri, Andrew, Burstein, Frada, Warren, Jim, Daly, Sharon, Patterson, Louise, Yearwood, John, Wolff, Alan
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
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- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
- Full Text:
- Reviewed:
- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
A technique for ranking friendship closeness in social networking services
- Sun, Zhaohao, Yearwood, John, Firmin, Sally
- Authors: Sun, Zhaohao , Yearwood, John , Firmin, Sally
- Date: 2013
- Type: Text , Conference paper
- Relation: 24th Australasian Conference on Information Systems (ACIS) p. 1-9
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- Description: The concept of friend and friendship are critical to both theoretical and empirical studies of social relations, social media and social networks. Measuring the closeness among friends is a big issue for developing online social networking services (SNS) such as Facebook. This paper will address this issue by proposing a technique for ranking friendship closeness in SNS. The technique consists of an algorithm for ranking need-driven friendship closeness and an algorithm for behaviour-based friendship closeness in online social networking sites. The former is based on Maslow’s hierarchy of needs, while the latter is based on behaviours of users on Facebook and TOPSIS. Examples provided illustrate the viability of the proposed algorithms. The research in this paper shows that ranking friendship closeness will facilitate understanding of needs and behaviours of friends and of friendships in SNS. The proposed approach will facilitate research and development of social media, social commerce, social networks, and SN
- Authors: Sun, Zhaohao , Yearwood, John , Firmin, Sally
- Date: 2013
- Type: Text , Conference paper
- Relation: 24th Australasian Conference on Information Systems (ACIS) p. 1-9
- Full Text:
- Reviewed:
- Description: The concept of friend and friendship are critical to both theoretical and empirical studies of social relations, social media and social networks. Measuring the closeness among friends is a big issue for developing online social networking services (SNS) such as Facebook. This paper will address this issue by proposing a technique for ranking friendship closeness in SNS. The technique consists of an algorithm for ranking need-driven friendship closeness and an algorithm for behaviour-based friendship closeness in online social networking sites. The former is based on Maslow’s hierarchy of needs, while the latter is based on behaviours of users on Facebook and TOPSIS. Examples provided illustrate the viability of the proposed algorithms. The research in this paper shows that ranking friendship closeness will facilitate understanding of needs and behaviours of friends and of friendships in SNS. The proposed approach will facilitate research and development of social media, social commerce, social networks, and SN
Attribute weighted Naive Bayes classifier using a local optimization
- Taheri, Sona, Yearwood, John, Mammadov, Musa, Seifollahi, Sattar
- Authors: Taheri, Sona , Yearwood, John , Mammadov, Musa , Seifollahi, Sattar
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol.24, no.5 (2013), p.995-1002
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- Description: The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.
- Authors: Taheri, Sona , Yearwood, John , Mammadov, Musa , Seifollahi, Sattar
- Date: 2013
- Type: Text , Journal article
- Relation: Neural Computing & Applications Vol.24, no.5 (2013), p.995-1002
- Full Text:
- Reviewed:
- Description: The Naive Bayes classifier is a popular classification technique for data mining and machine learning. It has been shown to be very effective on a variety of data classification problems. However, the strong assumption that all attributes are conditionally independent given the class is often violated in real-world applications. Numerous methods have been proposed in order to improve the performance of the Naive Bayes classifier by alleviating the attribute independence assumption. However, violation of the independence assumption can increase the expected error. Another alternative is assigning the weights for attributes. In this paper, we propose a novel attribute weighted Naive Bayes classifier by considering weights to the conditional probabilities. An objective function is modeled and taken into account, which is based on the structure of the Naive Bayes classifier and the attribute weights. The optimal weights are determined by a local optimization method using the quasisecant method. In the proposed approach, the Naive Bayes classifier is taken as a starting point. We report the results of numerical experiments on several real-world data sets in binary classification, which show the efficiency of the proposed method.
Illicit image detection : An MRF model based stochastic approach
- Islam, Mofakharul, Watters, Paul, Yearwood, John, Hussain, Mazher, Swarna, Lubaba
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 467-479
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- Description: The steady growth of the Internet, sophisticated digital image processing technology, the cheap availability of storage devices and surfer's ever-increasing interest on images have been contributing to make the Internet an unprecedented large image library. As a result, The Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the arrival of GPRS mobile telephone technology, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, driving us toward a new degree of complexity. In this paper, we propose a stochastic model based novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on contextual constraints that are representatives of actual pornographic activity. Compared to the results published in recent works, our proposed approach yields the highest accuracy in detection. © 2013 Springer Science+Business Media.
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 467-479
- Full Text:
- Reviewed:
- Description: The steady growth of the Internet, sophisticated digital image processing technology, the cheap availability of storage devices and surfer's ever-increasing interest on images have been contributing to make the Internet an unprecedented large image library. As a result, The Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the arrival of GPRS mobile telephone technology, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, driving us toward a new degree of complexity. In this paper, we propose a stochastic model based novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on contextual constraints that are representatives of actual pornographic activity. Compared to the results published in recent works, our proposed approach yields the highest accuracy in detection. © 2013 Springer Science+Business Media.
Illicit image detection using erotic pose estimation based on kinematic constraints
- Islam, Mofakharul, Watters, Paul, Yearwood, John, Hussain, Mazher, Swarna, Lubaba
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 481-495
- Full Text:
- Reviewed:
- Description: With the advent of the Internet along with sophisticated digital image processing technology, the Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the advent of GPRS mobile telephone networks, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, drives us toward a new degree of complexity. The detection of pornography remains an important and significant research problem, since there is great potential to minimize harm to the community. In this paper, we propose a novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on most commonly found erotic poses. Compared to the results published in recent works, our proposed approach yields the highest accuracy in recognition. © 2013 Springer Science+Business Media.
- Authors: Islam, Mofakharul , Watters, Paul , Yearwood, John , Hussain, Mazher , Swarna, Lubaba
- Date: 2013
- Type: Text , Book chapter
- Relation: Innovations and Advances in Computer, Information, Systems Sciences, and Engineering p. 481-495
- Full Text:
- Reviewed:
- Description: With the advent of the Internet along with sophisticated digital image processing technology, the Internet quickly became the principal medium for the distribution of pornographic content favouring pornography to become a drug of the millennium. With the advent of GPRS mobile telephone networks, and with the large scale arrival of the 3G networks, along with the cheap availability of latest mobile sets and a variety of forms of wireless connections, the internet has already gone to mobile, drives us toward a new degree of complexity. The detection of pornography remains an important and significant research problem, since there is great potential to minimize harm to the community. In this paper, we propose a novel approach to investigate and implement a pornography detection technique towards a framework for automated detection of pornography based on most commonly found erotic poses. Compared to the results published in recent works, our proposed approach yields the highest accuracy in recognition. © 2013 Springer Science+Business Media.
A comparison of machine learning algorithms for multilabel classification of CAN
- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Journal article
- Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
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- Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Journal article
- Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
- Full Text:
- Reviewed:
- Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.
Application of rank correlation, clustering and classification in information security
- Beliakov, Gleb, Yearwood, John, Kelarev, Andrei
- Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Networks Vol. 7, no. 6 (2012), p. 935-945
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- Description: This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman-Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms. © 2012 Academy Publisher.
- Description: 2003010277
- Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Networks Vol. 7, no. 6 (2012), p. 935-945
- Full Text:
- Reviewed:
- Description: This article is devoted to experimental investigation of a novel application of a clustering technique introduced by the authors recently in order to use robust and stable consensus functions in information security, where it is often necessary to process large data sets and monitor outcomes in real time, as it is required, for example, for intrusion detection. Here we concentrate on a particular case of application to profiling of phishing websites. First, we apply several independent clustering algorithms to a randomized sample of data to obtain independent initial clusterings. Silhouette index is used to determine the number of clusters. Second, rank correlation is used to select a subset of features for dimensionality reduction. We investigate the effectiveness of the Pearson Linear Correlation Coefficient, the Spearman Rank Correlation Coefficient and the Goodman-Kruskal Correlation Coefficient in this application. Third, we use a consensus function to combine independent initial clusterings into one consensus clustering. Fourth, we train fast supervised classification algorithms on the resulting consensus clustering in order to enable them to process the whole large data set as well as new data. The precision and recall of classifiers at the final stage of this scheme are critical for effectiveness of the whole procedure. We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms. © 2012 Academy Publisher.
- Description: 2003010277
Derivative-free optimization and neural networks for robust regression
- Beliakov, Gleb, Kelarev, Andrei, Yearwood, John
- Authors: Beliakov, Gleb , Kelarev, Andrei , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: Optimization Vol. 61, no. 12 (2012), p. 1467-1490
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- Description: Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression. © 2012 Copyright Taylor and Francis Group, LLC.
- Authors: Beliakov, Gleb , Kelarev, Andrei , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: Optimization Vol. 61, no. 12 (2012), p. 1467-1490
- Full Text:
- Reviewed:
- Description: Large outliers break down linear and nonlinear regression models. Robust regression methods allow one to filter out the outliers when building a model. By replacing the traditional least squares criterion with the least trimmed squares (LTS) criterion, in which half of data is treated as potential outliers, one can fit accurate regression models to strongly contaminated data. High-breakdown methods have become very well established in linear regression, but have started being applied for non-linear regression only recently. In this work, we examine the problem of fitting artificial neural networks (ANNs) to contaminated data using LTS criterion. We introduce a penalized LTS criterion which prevents unnecessary removal of valid data. Training of ANNs leads to a challenging non-smooth global optimization problem. We compare the efficiency of several derivative-free optimization methods in solving it, and show that our approach identifies the outliers correctly when ANNs are used for nonlinear regression. © 2012 Copyright Taylor and Francis Group, LLC.
Integrating online social networks with e-Commerce : A CBR approach
- Sun, Zhaohao, Firmin, Sally, Yearwood, John
- Authors: Sun, Zhaohao , Firmin, Sally , Yearwood, John
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Integrating online social networks (OSN) with e-commerce is a part of Enterprise 2.0 and social media and is of significance for development of e-commerce and online social networking services. However, how to integrate online social networks including Facebook with e-commerce is still a big issue for companies. Case based reasoning (CBR) has a number of successful applications in e-commerce and web services. This article examines how to integrate OSN with e-commerce, how to integrate CBR with e-commerce and how to integrate CBR with OSN. This article also proposes a CBR architecture for integrating online social networks with e-commerce using CBR as an intelligent intermediary. One of the research findings indicates that the principle of CBR is a useful marketing strategy for integrating e-commerce and OSN. The approach proposed in this research will facilitate the development of e-commerce, Enterprise 3.0 and online social networking services. Sun, Firmin, & Yearwood © 2012.
- Description: 2003010901
- Authors: Sun, Zhaohao , Firmin, Sally , Yearwood, John
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: Integrating online social networks (OSN) with e-commerce is a part of Enterprise 2.0 and social media and is of significance for development of e-commerce and online social networking services. However, how to integrate online social networks including Facebook with e-commerce is still a big issue for companies. Case based reasoning (CBR) has a number of successful applications in e-commerce and web services. This article examines how to integrate OSN with e-commerce, how to integrate CBR with e-commerce and how to integrate CBR with OSN. This article also proposes a CBR architecture for integrating online social networks with e-commerce using CBR as an intelligent intermediary. One of the research findings indicates that the principle of CBR is a useful marketing strategy for integrating e-commerce and OSN. The approach proposed in this research will facilitate the development of e-commerce, Enterprise 3.0 and online social networking services. Sun, Firmin, & Yearwood © 2012.
- Description: 2003010901
Optimal rees matrix constructions for analysis of data
- Kelarev, Andrei, Yearwood, John, Zi, Lifang
- Authors: Kelarev, Andrei , Yearwood, John , Zi, Lifang
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of the Australian Mathematical Society Vol. 92, no. 3 (2012), p. 357-366
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Relation: http://purl.org/au-research/grants/arc/DP0211866
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- Description: Abstract We introduce a new construction involving Rees matrix semigroups and max-plus algebras that is very convenient for generating sets of centroids. We describe completely all optimal sets of centroids for all Rees matrix semigroups without any restrictions on the sandwich matrices. © 2013 Australian Mathematical Publishing Association Inc.
- Description: 2003010862
- Authors: Kelarev, Andrei , Yearwood, John , Zi, Lifang
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of the Australian Mathematical Society Vol. 92, no. 3 (2012), p. 357-366
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Relation: http://purl.org/au-research/grants/arc/DP0211866
- Full Text:
- Reviewed:
- Description: Abstract We introduce a new construction involving Rees matrix semigroups and max-plus algebras that is very convenient for generating sets of centroids. We describe completely all optimal sets of centroids for all Rees matrix semigroups without any restrictions on the sandwich matrices. © 2013 Australian Mathematical Publishing Association Inc.
- Description: 2003010862
Performance evaluation of multi-tier ensemble classifiers for phishing websites
- Abawajy, Jemal, Beliakov, Gleb, Kelarev, Andrei, Yearwood, John
- Authors: Abawajy, Jemal , Beliakov, Gleb , Kelarev, Andrei , Yearwood, John
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the toptier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi-tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi-level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.
- Authors: Abawajy, Jemal , Beliakov, Gleb , Kelarev, Andrei , Yearwood, John
- Date: 2012
- Type: Text , Conference proceedings
- Full Text:
- Description: This article is devoted to large multi-tier ensemble classifiers generated as ensembles of ensembles and applied to phishing websites. Our new ensemble construction is a special case of the general and productive multi-tier approach well known in information security. Many efficient multi-tier classifiers have been considered in the literature. Our new contribution is in generating new large systems as ensembles of ensembles by linking a top-tier ensemble to another middletier ensemble instead of a base classifier so that the toptier ensemble can generate the whole system. This automatic generation capability includes many large ensemble classifiers in two tiers simultaneously and automatically combines them into one hierarchical unified system so that one ensemble is an integral part of another one. This new construction makes it easy to set up and run such large systems. The present article concentrates on the investigation of performance of these new multi-tier ensembles for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of multi-level ensembles with three tiers. The results presented here demonstrate that new three-tier ensemble classifiers performed better than the base classifiers and standard ensembles included in the system. This example of application to the classification of phishing websites shows that the new method of combining diverse ensemble techniques into a unified hierarchical three-tier ensemble can be applied to increase the performance of classifiers in situations where data can be processed on a large computer.
Rule-based classifiers and meta classifiers for identification of cardiac autonomic neuropathy progression
- Jelinek, Herbert, Kelarev, Andrei, Stranieri, Andrew, Yearwood, John
- Authors: Jelinek, Herbert , Kelarev, Andrei , Stranieri, Andrew , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Information Science and Computer Mathematics Vol. 5, no. 2 (2012), p. 49-53
- Full Text:
- Reviewed:
- Description: We investigate and compare several rule-based classifiers and meta classifiers in their ability to obtain multi-class classifications of cardiac autonomic neuropathy (CAN) and its progression. The best results obtained in our experiments are significantly better than the outcomes published previously in the literature for analogous CAN identification tasks or simpler binary classification tasks.
- Authors: Jelinek, Herbert , Kelarev, Andrei , Stranieri, Andrew , Yearwood, John
- Date: 2012
- Type: Text , Journal article
- Relation: International Journal of Information Science and Computer Mathematics Vol. 5, no. 2 (2012), p. 49-53
- Full Text:
- Reviewed:
- Description: We investigate and compare several rule-based classifiers and meta classifiers in their ability to obtain multi-class classifications of cardiac autonomic neuropathy (CAN) and its progression. The best results obtained in our experiments are significantly better than the outcomes published previously in the literature for analogous CAN identification tasks or simpler binary classification tasks.
Optimization and matrix constructions for classification of data
- Kelarev, Andrei, Yearwood, John, Vamplew, Peter, Abawajy, Jemal, Chowdhury, Morshed
- Authors: Kelarev, Andrei , Yearwood, John , Vamplew, Peter , Abawajy, Jemal , Chowdhury, Morshed
- Date: 2011
- Type: Journal article
- Relation: New Zealand Journal of Mathematics Vol. 41, no. 2011 (2011), p. 65-73
- Full Text:
- Reviewed:
- Description: Max-plus alegbras and more general semirings have many useful applications and have been actively investigated. On the other hand, structural matrix rings are also well known and have been considered by many authors. The main theorem of this article completely describes all optimal ideas in the more general structural matrix semirings. Originally, our investigation of these ideals was motivated by applications in data mining for the design of multiple classification systems combining several individual classifiers.
- Authors: Kelarev, Andrei , Yearwood, John , Vamplew, Peter , Abawajy, Jemal , Chowdhury, Morshed
- Date: 2011
- Type: Journal article
- Relation: New Zealand Journal of Mathematics Vol. 41, no. 2011 (2011), p. 65-73
- Full Text:
- Reviewed:
- Description: Max-plus alegbras and more general semirings have many useful applications and have been actively investigated. On the other hand, structural matrix rings are also well known and have been considered by many authors. The main theorem of this article completely describes all optimal ideas in the more general structural matrix semirings. Originally, our investigation of these ideals was motivated by applications in data mining for the design of multiple classification systems combining several individual classifiers.
Optimization of classifiers for data mining based on combinatorial semigroups
- Kelarev, Andrei, Yearwood, John, Watters, Paul
- Authors: Kelarev, Andrei , Yearwood, John , Watters, Paul
- Date: 2011
- Type: Text , Journal article
- Relation: Semigroup Forum Vol. 82, no. 2 (2011), p. 1-10
- Full Text:
- Reviewed:
- Description: The aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction. © 2011 Springer Science+Business Media, LLC.
- Authors: Kelarev, Andrei , Yearwood, John , Watters, Paul
- Date: 2011
- Type: Text , Journal article
- Relation: Semigroup Forum Vol. 82, no. 2 (2011), p. 1-10
- Full Text:
- Reviewed:
- Description: The aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction. © 2011 Springer Science+Business Media, LLC.
Optimization of matrix semirings for classification systems
- Gao, David, Kelarev, Andrei, Yearwood, John
- Authors: Gao, David , Kelarev, Andrei , Yearwood, John
- Date: 2011
- Type: Text , Journal article
- Relation: Bulletin of the Australian Mathematical Society Vol. 84, no. 3 (2011), p. 492-503
- Full Text:
- Reviewed:
- Description: The max-plus algebra is well known and has useful applications in the investigation of discrete event systems and affine equations. Structural matrix rings have been considered by many authors too. This article introduces more general structural matrix semirings, which include all matrix semirings over the max-plus algebra. We investigate properties of ideals in this construction motivated by applications to the design of centroid-based classification systems, or classifiers, as well as multiple classifiers combining several initial classifiers. The first main theorem of this paper shows that structural matrix semirings possess convenient visible generating sets for ideals. Our second main theorem uses two special sets to determine the weights of all ideals and describe all matrix ideals with the largest possible weight, which are optimal for the design of classification systems. © Copyright Australian Mathematical Publishing Association Inc. 2011.
- Description: 2003009498
- Authors: Gao, David , Kelarev, Andrei , Yearwood, John
- Date: 2011
- Type: Text , Journal article
- Relation: Bulletin of the Australian Mathematical Society Vol. 84, no. 3 (2011), p. 492-503
- Full Text:
- Reviewed:
- Description: The max-plus algebra is well known and has useful applications in the investigation of discrete event systems and affine equations. Structural matrix rings have been considered by many authors too. This article introduces more general structural matrix semirings, which include all matrix semirings over the max-plus algebra. We investigate properties of ideals in this construction motivated by applications to the design of centroid-based classification systems, or classifiers, as well as multiple classifiers combining several initial classifiers. The first main theorem of this paper shows that structural matrix semirings possess convenient visible generating sets for ideals. Our second main theorem uses two special sets to determine the weights of all ideals and describe all matrix ideals with the largest possible weight, which are optimal for the design of classification systems. © Copyright Australian Mathematical Publishing Association Inc. 2011.
- Description: 2003009498
A new supervised term ranking method for text categorization
- Mammadov, Musa, Yearwood, John, Zhao, Lei
- Authors: Mammadov, Musa , Yearwood, John , Zhao, Lei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 102-111
- Full Text:
- Reviewed:
- Description: In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better. © 2010 Springer-Verlag.
- Authors: Mammadov, Musa , Yearwood, John , Zhao, Lei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 Vol. 6464 LNAI, p. 102-111
- Full Text:
- Reviewed:
- Description: In text categorization, different supervised term weighting methods have been applied to improve classification performance by weighting terms with respect to different categories, for example, Information Gain, χ2 statistic, and Odds Ratio. From the literature there are three term ranking methods to summarize term weights of different categories for multi-class text categorization. They are Summation, Average, and Maximum methods. In this paper we present a new term ranking method to summarize term weights, i.e. Maximum Gap. Using two different methods of information gain and χ2 statistic, we setup controlled experiments for different term ranking methods. Reuter-21578 text corpus is used as the dataset. Two popular classification algorithms SVM and Boostexter are adopted to evaluate the performance of different term ranking methods. Experimental results show that the new term ranking method performs better. © 2010 Springer-Verlag.
Adaptive clustering with feature ranking for DDoS attacks detection
- Zi, Lifang, Yearwood, John, Wu, Xin
- Authors: Zi, Lifang , Yearwood, John , Wu, Xin
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE.
- Authors: Zi, Lifang , Yearwood, John , Wu, Xin
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Distributed Denial of Service (DDoS) attacks pose an increasing threat to the current internet. The detection of such attacks plays an important role in maintaining the security of networks. In this paper, we propose a novel adaptive clustering method combined with feature ranking for DDoS attacks detection. First, based on the analysis of network traffic, preliminary variables are selected. Second, the Modified Global K-means algorithm (MGKM) is used as the basic incremental clustering algorithm to identify the cluster structure of the target data. Third, the linear correlation coefficient is used for feature ranking. Lastly, the feature ranking result is used to inform and recalculate the clusters. This adaptive process can make worthwhile adjustments to the working feature vector according to different patterns of DDoS attacks, and can improve the quality of the clusters and the effectiveness of the clustering algorithm. The experimental results demonstrate that our method is effective and adaptive in detecting the separate phases of DDoS attacks. © 2010 IEEE.
An application of consensus clustering for DDoS attacks detection
- Zi, Lifang, Yearwood, John, Kelarev, Andrei
- Authors: Zi, Lifang , Yearwood, John , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
- Authors: Zi, Lifang , Yearwood, John , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
Cluster based rule discovery model for enhancement of government's tobacco control strategy
- Huda, Shamsul, Yearwood, John, Borland, Ron
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Discovery of interesting rules describing the behavioural patterns of smokers' quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers' quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The clusterbased approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers' quitting intention more accurately than a single decision tree. © 2010 IEEE.
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: Discovery of interesting rules describing the behavioural patterns of smokers' quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers' quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The clusterbased approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers' quitting intention more accurately than a single decision tree. © 2010 IEEE.
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
- Dazeley, Richard, Yearwood, John, Kang, Byeongho, Kelarev, Andrei
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
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- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.
- Authors: Dazeley, Richard , Yearwood, John , Kang, Byeongho , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 11th International Workshop on Knowledge Management and Acquisition for Smart Systems and Services, PKAW 2010 Vol. 6232 LNAI, p. 235-246
- Full Text:
- Reviewed:
- Description: This article investigates internet commerce security applications of a novel combined method, which uses unsupervised consensus clustering algorithms in combination with supervised classification methods. First, a variety of independent clustering algorithms are applied to a randomized sample of data. Second, several consensus functions and sophisticated algorithms are used to combine these independent clusterings into one final consensus clustering. Third, the consensus clustering of the randomized sample is used as a training set to train several fast supervised classification algorithms. Finally, these fast classification algorithms are used to classify the whole large data set. One of the advantages of this approach is in its ability to facilitate the inclusion of contributions from domain experts in order to adjust the training set created by consensus clustering. We apply this approach to profiling phishing emails selected from a very large data set supplied by the industry partners of the Centre for Informatics and Applied Optimization. Our experiments compare the performance of several classification algorithms incorporated in this scheme. © 2010 Springer-Verlag Berlin Heidelberg.