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Applications of machine learning for linguistic analysis of texts

- Torney, Rosemary, Yearwood, John, Vamplew, Peter, Kelarev, Andrei

  • Authors: Torney, Rosemary , Yearwood, John , Vamplew, Peter , Kelarev, Andrei
  • Date: 2012
  • Type: Text , Book chapter
  • Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 133-148
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  • Description: This chapter describes a novel multistage method for linguistic clustering of large collections of texts available on the Internet as a precursor to linguistic analysis of these texts. This method addresses the practicalities of applying clustering operations to a very large set of text documents by using a combination of unsupervised clustering and supervised classification. The method relies on creating a multitude of independent clusterings of a randomized sample selected from the International Corpus of Learner English. Several consensus functions and sophisticated algorithms are applied in two substages to combine these independent clusterings into one final consensus clustering, which is then used to train fast classifiers in order to enable them to perform the profiling of very large collections of text and web data. This approach makes it possible to apply advanced highly accurate and sophisticated clustering techniques by combining them with fast supervised classification algorithms. For the effectiveness of this multistage method it is crucial to determine how well the supervised classification algorithms are going to perform at the final stage, when they are used to process large data sets available on the Internet. This performance may also serve as an indication of the quality of the combined consensus clustering obtained in the preceding stages. The authors' experimental results compare the performance of several classification algorithms incorporated in this multistage scheme and demonstrate that several of these classification algorithms achieve very high precision and recall and can be used in practical implementations of their method.

Approaches for community decision making and collective reasoning: Knowledge technology support

- Yearwood, John, Stranieri, Andrew

  • Authors: Yearwood, John , Stranieri, Andrew
  • Date: 2012
  • Type: Text , Book
  • Relation: Approaches for Community Decision Making and Collective Reasoning: Knowledge Technology Support
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  • Description: Technology currently encourages the capture and storage of vast quantities of data and information and so thinkers, reasoners, and decision-makers have available large resources to support their tasks. At the same time, there is a need to engage with an enormous range of complex issues that require reasoning and decisions that are actionable to address them. Approaches for Community Decision Making and Collective Reasoning: Knowledge Technology Support acts to provide knowledge for each individual in a group with the broad structural wealth of reasoning. It also acts as an explicit structure that technological devices for supporting reasoning within a group can hook onto. If you are interested in how groups can structure their activities towards making better decisions or in developing technologies for the support of decision-making in groups, then this book is an excellent way to understand the state of the art and possible ways forward.

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.

Detection of CAN by ensemble classifiers based on Ripple Down rules

- Kelarev, Andrei, Dazeley, Richard, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Dazeley, Richard , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Book chapter
  • Relation: Knowledge Management and Acquisition for Intelligent Systems p. 147-159
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  • Description: It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.

Empirical investigation of consensus clustering for large ECG data sets

- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: This article investigates a novel machine learning approach applying consensus clustering in conjunction with classification for the data mining of very large and highly dimensional ECG data sets. To obtain robust and stable clusterings, consensus functions can be applied for clustering ensembles combining a multitude of independent initial clusterings. Direct applications of consensus functions to highly dimensional ECG data sets remain computationally expensive and impracticable. We introduce a multistage scheme including various procedures for dimensionality reduction, consensus clustering of randomized samples, followed by the use of a fast supervised classification algorithm. Applying the Hybrid Bipartite Graph Formulation combined with rank ordering and SMO we obtained an area under the receiver operating curve of 0.987. The performance of the classification algorithm at the final stage is crucial for the effectiveness of this technique. It can be regarded as an indication of the reliability, quality and stability of the combined consensus clustering. © 2012 IEEE.

Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare

- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Conference proceedings
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  • Description: Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature. © 2012 IEEE.
  • Description: 2003009675

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

- Kelarev, Andrei, Stranieri, Andrew, Abawajy, Jemal, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Abawajy, Jemal , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Conference paper
  • Relation: Tenth Australasian Data Mining Conference Vol. 134, p. 93-101
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  • Description: This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

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
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  • 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

Machine learning algorithms for analysis of DNA data sets

- Yearwood, John, Bagirov, Adil, Kelarev, Andrei

  • Authors: Yearwood, John , Bagirov, Adil , Kelarev, Andrei
  • Date: 2012
  • Type: Text , Book chapter
  • Relation: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques p. 47-58
  • Relation: http://purl.org/au-research/grants/arc/LP0990908
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  • Description: The applications of machine learning algorithms to the analysis of data sets of DNA sequences are very important. The present chapter is devoted to the experimental investigation of applications of several machine learning algorithms for the analysis of a JLA data set consisting of DNA sequences derived from non-coding segments in the junction of the large single copy region and inverted repeat A of the chloroplast genome in Eucalyptus collected by Australian biologists. Data sets of this sort represent a new situation, where sophisticated alignment scores have to be used as a measure of similarity. The alignment scores do not satisfy properties of the Minkowski metric, and new machine learning approaches have to be investigated. The authors' experiments show that machine learning algorithms based on local alignment scores achieve very good agreement with known biological classes for this data set. A new machine learning algorithm based on graph partitioning performed best for clustering of the JLA data set. Our novel k-committees algorithm produced most accurate results for classification. Two new examples of synthetic data sets demonstrate that the authors' k-committees algorithm can outperform both the Nearest Neighbour and k-medoids algorithms simultaneously.

Novel weighting in single hidden layer feedforward neural networks for data classification

- Seifollahi, Sattar, Yearwood, John, Ofoghi, Bahadorreza

  • Authors: Seifollahi, Sattar , Yearwood, John , Ofoghi, Bahadorreza
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Computers and Mathematics with Applications Vol. 64, no. 2 (2012), p. 128-136
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  • Description: We propose a binary classifier based on the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden layer. We use a modified attribute-class correlation measure to determine the weights of attributes in the networks. Moreover, we propose new weights called as influence weights to utilize in the weights connecting the input layer and the hidden layer nodes (hidden weights) of the network with sigmoid hidden nodes. These weights are calculated as the sum of conditional probabilities of attribute values given class labels. Our learning procedure of the networks is based on the extreme learning machines; in which the parameters of the hidden nodes are first calculated and then the weights connecting the hidden nodes and output nodes (output weights) are found. The results of the networks with the proposed weights on some benchmark data sets show improvements over those of the conventional networks. © 2012 Elsevier Ltd. All rights reserved.

Optimal rees matrix constructions for analysis of data

- Kelarev, Andrei, Yearwood, John, Zi, Lifang

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
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  • 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.

Profiling phishing activity based on hyperlinks extracted from phishing emails

- Yearwood, John, Mammadov, Musa, Webb, Dean

  • Authors: Yearwood, John , Mammadov, Musa , Webb, Dean
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Social Network Analysis and Mining Vol. 2, no. 1 (2012), p. 5-16
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  • Description: Phishing activity has recently been focused on social networking sites as a more effective way of exploiting not only the technology but also the trust that may exist between members in a social network. In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling problem as a multi-label classification problem using the hyperlinks in the phishing emails as features and structural properties of emails along with whois (i.e. DNS) information on hyperlinks as profile classes. Further, we generate profiles based on the classifier predictions. Thus, classes become elements of profiles. We employ a boosting algorithm (AdaBoost) as well as SVM to generate multi-label class predictions on three different datasets created from hyperlink information in phishing emails. These predictions are further utilized to generate complete profiles of these emails. Results show that profiling can be done with quite high accuracy using hyperlink information.

Rule-based classifiers and meta classifiers for identification of cardiac autonomic neuropathy progression

- Jelinek, Herbert, Kelarev, Andrei, Stranieri, Andrew, Yearwood, John

Using psycholinguistic features for profiling first language of authors

- Torney, Rosemary, Vamplew, Peter, Yearwood, John

  • Authors: Torney, Rosemary , Vamplew, Peter , Yearwood, John
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Journal of the American Society for Information Science and Technology Vol. 63, no. 6 (2012), p. 1256-1269
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  • Description: This study empirically evaluates the effectiveness of different feature types for the classification of the first language of an author. In particular, it examines the utility of psycholinguistic features, extracted by the Linguistic Inquiry and Word Count (LIWC) tool, that have not previously been applied to the task of author profiling. As LIWC is a tool that has been developed in the psycholinguistic field rather than the computational linguistics field, it was hypothesized that it would be effective, both as a single type feature set because of its psycholinguistic basis, and in combination with other feature sets, because it should be sufficiently different to add insight rather than redundancy. It was found that LIWC features were competitive with previously used feature types in identifying the first language of an author, and that combined feature sets including LIWC features consistently showed better accuracy rates and average F measures than were achieved by the same feature sets without the LIWC features. As a secondary issue, this study also examined how effectively first language classification scaled up to a larger number of possible languages. It was found that the classification scheme scaled up effectively to the entire 16 language collection from the International Corpus of Learner English, when compared with results achieved on just 5 languages in previous research. 2012 ASIS&T.

Water allocation argument tree (WAAT): A tool for facilitating public participation in water allocation decisions

- Graymore, Michelle, Stranieri, Andrew, McRae-Williams, Pamela, Mays, Heather, Lehmann, La Vergne, Thoms, Gavin, Yearwood, John

A case for the re-use of community reasoning

- Stranieri, Andrew, Yearwood, John

  • Authors: Stranieri, Andrew , Yearwood, John
  • Date: 2011
  • Type: Text , Book chapter
  • Relation: Technologies for supporting reasoning communities and collaborative decision making: Cooperative approaches p.
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  • Description: In software engineering, the re-use concept is a design principle that improves efficency, quality and maintainability by ensuring that software artifacts are developed once and re-used may times. In an analogous way, a group's reasoning can be imagined to be re-used by that or another group to enhance efficiency, transparency and consistency in decison-making. However, the re-use of reasoning is difficult to achieve because group reasoning cannot easily be captured and the way in which a group reasoning artifact is subsequently used is not obvious. This chapter explores the case for the re-use of community reasoning and concludes that individuals can benefit from a representation of a previous groups's coalesced reasoning to be modeled and the scheme to represent the reasoning have been selected to suit the task. The authors contend that specifying the future community like to re-use the reasoning, called the intended audience, informs a decision regarding whether an exercise aimed at coalescing a group's reasoning is best performed verbally, in writing or with the use of more structured schemes such as Argument visualization.

A Grobner-Shirshov Algorithm for Applications in Internet Security

- Kelarev, Andrei, Yearwood, John, Watters, Paul, Wu, Xinwen, Ma, Liping, Abawajy, Jemal, Pan, L.

A novel canonical dual computational approach for prion AGAAAAGA amyloid fibril molecular modeling

- Zhang, Jiapu, Gao, David, Yearwood, John

  • Authors: Zhang, Jiapu , Gao, David , Yearwood, John
  • Date: 2011
  • Type: Text , Journal article
  • Relation: Journal of Theoretical Biology Vol. 284, no. 1 (2011), p. 149-157
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  • Description: Many experimental studies have shown that the prion AGAAAAGA palindrome hydrophobic region (113-120) has amyloid fibril forming properties and plays an important role in prion diseases. However, due to the unstable, noncrystalline and insoluble nature of the amyloid fibril, to date structural information on AGAAAAGA region (113-120) has been very limited. This region falls just within the N-terminal unstructured region PrP (1-123) of prion proteins. Traditional X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy experimental methods cannot be used to get its structural information. Under this background, this paper introduces a novel approach of the canonical dual theory to address the 3D atomic-resolution structure of prion AGAAAAGA amyloid fibrils. The novel and powerful canonical dual computational approach introduced in this paper is for the molecular modeling of prion AGAAAAGA amyloid fibrils, and that the optimal atomic-resolution structures of prion AGAAAAGA amyloid fibils presented in this paper are useful for the drive to find treatments for prion diseases in the field of medicinal chemistry. Overall, this paper presents an important method and provides useful information for treatments of prion diseases. © 2011.

A novel hybrid neural learning algorithm using simulated annealing and quasisecant method

- Yearwood, John, Bagirov, Adil, Seifollahi, Sattar

  • Authors: Yearwood, John , Bagirov, Adil , Seifollahi, Sattar
  • Date: 2011
  • Type: Text , Conference proceedings
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  • Description: In this paper, we propose a hybrid learning algorithm for the single hidden layer feedforward neural networks (SLFNs) for data classification. The proposed hybrid algorithm is a two-phase learning algorithm and is based on the quasisecant and the simulated annealing methods. First, the weights between the hidden layer and the output layer nodes (output layer weights) are adjusted by the quasisecant algorithm. Then the simulated annealing is applied for global attribute weighting. The weights between the input layer and the hidden layer nodes are fixed in advance and are not included in the learning process. The proposed two-phase learning of the network is a novel idea and is different from that of the existing ones. The numerical results on some benchmark data sets are also reported and these results are promising. © 2011, Australian Computer Society, Inc.
  • Description: 2003009507