A comparison of machine learning algorithms for multilabel classification of CAN
- 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.
A data mining application of the incidence semirings
- Authors: Abawajy, Jemal , Kelarev, Andrei , Yearwood, John , Turville, Christopher
- Date: 2013
- Type: Text , Journal article
- Relation: Houston Journal of Mathematics Vol. 39, no. 4 (2013), p. 1083-1093
- Relation: http://purl.org/au-research/grants/arc/LP0990908
- Full Text: false
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- Description: This paper is devoted to a combinatorial problem for incidence semirings, which can be viewed as sets of polynomials over graphs, where the edges are the unknowns and the coefficients are taken from a semiring. The construction of incidence rings is very well known and has many useful applications. The present article is devoted to a novel application of the more general incidence semirings. Recent research on data mining has motivated the investigation of the sets of centroids that have largest weights in semiring constructions. These sets are valuable for the design of centroid-based classification systems, or classifiers, as well as for the design of multiple classifiers combining several individual classifiers. Our article gives a complete description of all sets of centroids with the largest weight in incidence semirings.
A description of incidence rings of group automata
- Authors: Kelarev, Andrei , Passman, D.S.
- Date: 2008
- Type: Text , Book chapter
- Relation: Contemporary Mathematics : Noncommutative Rings, Group Rings, Diagram Algebras and Their Applications : International Conference December 18-22, 2006, University of Madras, Chennai, India Chapter p. 27-33
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- Description: Group automata occur in the Krohn-Rhodes Decomposition Theorem and have been extensively investigated in the literature. The incidence rings of group automata were introduced by the first author in analogy with group rings and incidence rings of graphs. The main theorem of the present paper gives a complete description of the structure of incidence rings of group automata in terms of matrix rings over group rings and their natural modules. As a consequence, when the ground ring is a field, we can use known group algebra results to determine when the incidence algebra is prime, semiprime, Artinian or semisimple. We also offer sufficient conditions for the algebra to be semiprimitive.
- Description: 2003006588
A formula for multiple classifiers in data mining based on Brandt semigroups
- Authors: Kelarev, Andrei , Yearwood, John , Mammadov, Musa
- Date: 2009
- Type: Text , Journal article
- Relation: Semigroup Forum Vol. 78, no. 2 (2009), p. 293-309
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- Description: A general approach to designing multiple classifiers represents them as a combination of several binary classifiers in order to enable correction of classification errors and increase reliability. This method is explained, for example, in Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques, 2005, Sect. 7.5). The aim of this paper is to investigate representations of this sort based on Brandt semigroups. We give a formula for the maximum number of errors of binary classifiers, which can be corrected by a multiple classifier of this type. Examples show that our formula does not carry over to larger classes of semigroups. © 2008 Springer Science+Business Media, LLC.
A Grobner-Shirshov Algorithm for Applications in Internet Security
- Authors: Kelarev, Andrei , Yearwood, John , Watters, Paul , Wu, Xinwen , Ma, Liping , Abawajy, Jemal , Pan, L.
- Date: 2011
- Type: Text , Journal article
- Relation: Southeast Asian Bulletin of Mathematics Vol. 35, no. (2011), p. 807-820
- Full Text: false
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- Description: The design of multiple classication and clustering systems for the detection of malware is an important problem in internet security. Grobner-Shirshov bases have been used recently by Dazeley et al. [15] to develop an algorithm for constructions with certain restrictions on the sandwich-matrices. We develop a new Grobner-Shirshov algorithm which applies to a larger variety of constructions based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrices.
A multi-tier ensemble construction of classifiers for phishing email detection and filtering
- Authors: Abawajy, Jemal , Kelarev, Andrei
- Date: 2012
- Type: Text , Conference paper
- Relation: 4th International Symposium on Cyberspace Safety and Security, CSS 2012 Vol. 7672 LNCS, p. 48-56
- Full Text: false
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- Description: This paper is devoted to multi-tier ensemble classifiers for the detection and filtering of phishing emails. We introduce a new construction of ensemble classifiers, based on the well known and productive multi-tier approach. Our experiments evaluate their performance for the detection and filtering of phishing emails. The multi-tier constructions are well known and have been used to design effective classifiers for email classification and other applications previously. We investigate new multi-tier ensemble classifiers, where diverse ensemble methods are combined in a unified system by incorporating different ensembles at a lower tier as an integral part of another ensemble at the top tier. Our novel contribution is to investigate the possibility and effectiveness of combining diverse ensemble methods into one large multi-tier ensemble for the example of detection and filtering of phishing emails. Our study handled a few essential ensemble methods and more recent approaches incorporated into a combined multi-tier ensemble classifier. The results show that new large multi-tier ensemble classifiers achieved better performance compared with the outcomes of the base classifiers and ensemble classifiers incorporated in the multi-tier system. This demonstrates that the new method of combining diverse ensembles into one unified multi-tier ensemble can be applied to increase the performance of classifiers if diverse ensembles are incorporated in the system. © 2012 Springer-Verlag.
- Description: 2003010675
A polynomial ring construction for the classification of data
- Authors: Kelarev, Andrei , Yearwood, John , Vamplew, Peter
- Date: 2009
- Type: Text , Journal article
- Relation: Bulletin of the Australian Mathematical Society Vol. 79, no. 2 (2009), p. 213-225
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- Description: Drensky and Lakatos (Lecture Notes in Computer Science, 357 (Springer, Berlin, 1989), pp. 181-188) have established a convenient property of certain ideals in polynomial quotient rings, which can now be used to determine error-correcting capabilities of combined multiple classifiers following a standard approach explained in the well-known monograph by Witten and Frank (Data Mining: Practical Machine Learning Tools and Techniques (Elsevier, Amsterdam, 2005)). We strengthen and generalise the result of Drensky and Lakatos by demonstrating that the corresponding nice property remains valid in a much larger variety of constructions and applies to more general types of ideals. Examples show that our theorems do not extend to larger classes of ring constructions and cannot be simplified or generalised.
An algorithm for BCH codes extended with finite state automata
- Authors: Kelarev, Andrei
- Date: 2008
- Type: Text , Journal article
- Relation: Fundamenta Informaticae Vol. 84, no. 1 (2008), p. 51-60
- Full Text: false
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- Description: This articles develops a combinatorial algorithm for a class of codes extending BCH codes and constructed with finite state automata. Ou algorithm computes the largest number of errors that the extended codes can correct, and finds a generator for each optimal code in this class of extensions. The question of finding codes with largest possible information rates remains open.
- Description: C1
An algorithm for the optimization of multiple classifers in data mining based on graphs
- Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
- Date: 2009
- Type: Text , Journal article
- Relation: The Journal of Combinatorial Mathematics and Combinatorial Computing Vol. 71, no. (2009), p. 65-85
- Full Text: false
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- Description: This article develops an efficient combinatorial algorithm based on labeled directed graphs and motivated by applications in data mining for designing multiple classifiers. Our method originates from the standard approach described in [37]. It defines a representation of a multiclass classifier in terms of several binary classifiers. We are using labeled graphs to introduce additional structure on the classifier. Representations of this sort are known to have serious advantages. An important property of these representations is their ability to correct errors of individual binary classifiers and produce correct combined output. For every representation like this we develop a combinatorial algorithm with quadratic running time to compute the largest number of errors of individual binary classifiers which can be corrected by the combined multiple classifier. In addition, we consider the question of optimizing the classifiers of this type and find all optimal representations for these multiple classifiers.
- Description: 2003007563
An application of consensus clustering for DDoS attacks detection
- Authors: Zi, Lifang , Yearwood, John , Kelarev, Andrei
- Date: 2010
- Type: Text , Conference proceedings
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- 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.
An application of novel clustering technique for information security
- Authors: Beliakov, Gleb , Yearwood, John , Kelarev, Andrei
- Date: 2011
- Type: Text , Conference paper
- Relation: Applications and Techniques in Information Security Workshop p. 5-11
- Full Text: false
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- Description: This article presents experimental results devoted to a new application of the novel clustering technique introduced by the authors recently. Our aim is to facilitate the application of 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 the 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, we use a consensus function to combine these independent clusterings into one consensus clustering . Feature ranking is used to select a subset of features for the consensus function. Third, 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 three consensus functions, Cluster-Based Graph Formulation (CBGF), Hybrid Bipartite Graph Formulation (HBGF), and Instance-Based Graph Formulation (IBGF) and a variety of supervised classification algorithms. The best precision and recall have been obtained by the combination of the HBGF consensus function and the SMO classifier with the polynomial kernel.
- Description: 2003009195
An approach for Ewing test selection to support the clinical assessment of cardiac autonomic neuropathy
- Authors: Stranieri, Andrew , Abawajy, Jemal , Kelarev, Andrei , Huda, Shamsul , Chowdhury, Morshed , Jelinek, Herbert
- Date: 2013
- Type: Text , Journal article
- Relation: Artificial Intelligence in Medicine Vol. 58, no. 3 (2013), p. 185-193
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- Description: Objective: This article addresses the problem of determining optimal sequences of tests for the clinical assessment of cardiac autonomic neuropathy (CAN) We investigate the accuracy of using only one of the recommended Ewing tests to classify CAN and the additional accuracy obtained by adding the remaining tests of the Ewing battery This is important as not all five Ewing tests can always be applied in each situation in practice Methods and material: We used new and unique database of the diabetes screening research initiative project, which is more than ten times larger than the data set used by Ewing in his original investigation of CAN We utilized decision trees and the optimal decision path finder (ODPF) procedure for identifying optimal sequences of tests Results: We present experimental results on the accuracy of using each one of the recommended Ewing tests to classify CAN and the additional accuracy that can be achieved by adding the remaining tests of the Ewing battery We found the best sequences of tests for cost-function equal to the number of tests The accuracies achieved by the initial segments of the optimal sequences for 2, 3 and 4 categories of CAN are 80.80, 91.33, 93.97 and 94.14, and respectively, 79.86, 89.29, 91.16 and 91.76, and 78.90, 86.21, 88.15 and 88.93 They show significant improvement compared to the sequence considered previously in the literature and the mathematical expectations of the accuracies of a random sequence of tests The complete outcomes obtained for all subsets of the Ewing features are required for determining optimal sequences of tests for any cost-function with the use of the ODPF procedure We have also found two most significant additional features that can increase the accuracy when some of the Ewing attributes cannot be obtained Conclusions: The outcomes obtained can be used to determine the optimal sequences of tests for each individual cost-function by following the ODPF procedure The results show that the best single Ewing test for diagnosing CAN is the deep breathing heart rate variation test Optimal sequences found for the cost-function equal to the number of tests guarantee that the best accuracy is achieved after any number of tests and provide an improvement in comparison with the previous ordering of tests or a random sequence © 2013 Elsevier B.V.
- Description: 2003011130
Application of rank correlation, clustering and classification in information security
- 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
Applications of machine learning for linguistic analysis of texts
- 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
- Full Text: false
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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.
Applying clustering and ensemble clustering approaches to phishing profiling
- Authors: Webb, Dean , Yearwood, John , Vamplew, Peter , Ma, Liping , Ofoghi, Bahadorreza , Kelarev, Andrei
- Date: 2009
- Type: Text , Conference paper
- Relation: Paper presented at Eighth Australasian Data Mining Conference, AusDM 2009, University of Melbourne, Melbourne, Victoria : 1st–4th December 2009
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- Description: 2003007911
Cayley graphs as classifiers for data mining : The influence of asymmetries
- Authors: Kelarev, Andrei , Ryan, Joe , Yearwood, John
- Date: 2009
- Type: Text , Journal article
- Relation: Discrete Mathematics Vol. 309, no. 17 (2009), p. 5360-5369
- Relation: http://purl.org/au-research/grants/arc/DP0211866
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- Description: The endomorphism monoids of graphs have been actively investigated. They are convenient tools expressing asymmetries of the graphs. One of the most important classes of graphs considered in this framework is that of Cayley graphs. Our paper proposes a new method of using Cayley graphs for classification of data. We give a survey of recent results devoted to the Cayley graphs also involving their endomorphism monoids. © 2008 Elsevier B.V. All rights reserved.
Classification systems based on combinatorial semigroups
- Authors: Abawajy, Jemal , Kelarev, Andrei
- Date: 2013
- Type: Text , Journal article
- Relation: Semigroup forum Vol. 86, no. 3 (2013), p. 603-612
- Full Text: false
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- Description: The present article continues the investigation of constructions essential for applications of combinatorial semigroups to the design of multiple classification systems in data mining. Our main theorem gives a complete description of all optimal classification systems defined by one-sided ideals in a construction based on combinatorial Rees matrix semigroups. It strengthens and generalizes previous results, which handled the more narrow case of two-sided ideals.
Classification systems based on combinatorial semigroups
- Authors: Abawajy, Jemal , Kelarev, Andrei
- Date: 2013
- Type: Text , Journal article
- Relation: Semigroup Forum Vol. 86, no. 3 (2013), p. 603-612
- Full Text:
- Reviewed:
- Description: The present article continues the investigation of constructions essential for applications of combinatorial semigroups to the design of multiple classification systems in data mining. Our main theorem gives a complete description of all optimal classification systems defined by one-sided ideals in a construction based on combinatorial Rees matrix semigroups. It strengthens and generalizes previous results, which handled the more narrow case of two-sided ideals. © 2012 Springer Science+Business Media New York.
- Description: 2003011021
Consensus clustering and supervised classification for profiling phishing emails in internet commerce security
- 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.
Constructing stochastic mixture policies for episodic multiobjective reinforcement learning tasks
- Authors: Vamplew, Peter , Dazeley, Richard , Barker, Ewan , Kelarev, Andrei
- Date: 2009
- Type: Text , Book chapter
- Relation: AI 2009 : Advances in Artificial Intelligence : 22nd Australasian Joint Conference, Melbourne, Australia, December 1-4, 2009. Proceedings Chapter p. 340-349
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- Description: Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobjective tasks from deterministic base policies found via scalarised reinforcement learning. It is shown that these approaches are an efficient means of identifying solutions which offer a superior match to the user’s preferences than can be achieved by methods based strictly on deterministic policies.
- Description: 2003007906