VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning

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dc.contributor.author Yu, T
dc.contributor.author Simoff, SJ
dc.contributor.author Jan, T
dc.date.accessioned 2011-02-07T06:24:40Z
dc.date.issued 2010-01
dc.identifier.citation Neurocomputing, 2010, 73 (13-15), pp. 2614 - 2623
dc.identifier.issn 0925-2312
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/13780
dc.description.abstract When dealing with real-world problems, there is considerable amount of prior domain knowledge that can provide insights on various aspect of the problem. On the other hand, many machine learning methods rely solely on the data sets for their learning phase and do not take into account any explicitly expressed domain knowledge. This paper proposes a framework that investigates and enables the incorporation of prior domain knowledge with respect to three key characteristics of inductive machine learning algorithms: consistency, generalization and convergence. The framework is used to review, classify and analyse key existing approaches to incorporating domain knowledge into inductive machine learning, as well as to consider the risks of doing so. The paper also demonstrates the design of a novel hierarchical semi-parametric machine learning method, capable of incorporating prior domain knowledge. The methodâVQSVMâextends the support vector machine (SVM) family of methods with vector quantization (VQ) techniques to address the problem of learning from imbalanced data sets. The paper presents the results of testing the method on a collection of imbalanced data sets with various imbalance ratios and various numbers of subclasses. The learning process of the VQSVM method utilizes some domain knowledge to solve problem of fitting imbalance data. The experiments in the paper demonstrate that enabling the incorporation of prior domain knowledge into the SVM framework is an effective way to overcome the sensitivity of SVM towards the imbalance ratio in a data set.
dc.format Esha Dutt
dc.publisher Elsevier Science B.V
dc.relation.isbasedon 10.1016/j.neucom.2010.05.007
dc.title VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning
dc.type Journal Article
dc.parent Neurocomputing
dc.journal.volume 13-15
dc.journal.volume 73
dc.journal.number 13-15 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 2614 en_US
dc.identifier.endpage 2623 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 110999 Neurosciences Not Elsewhere Classified
dc.personcode 000716
dc.personcode 020524
dc.percentage 100 en_US
dc.classification.name Neurosciences not elsewhere classified en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Prior domain knowledge; Inductive machine learning; Imbalance data; Support vector machine en_US
dc.description.keywords Prior domain knowledge
dc.description.keywords Prior domain knowledge
dc.description.keywords Inductive machine learning
dc.description.keywords Inductive machine learning
dc.description.keywords Imbalance data
dc.description.keywords Imbalance data
dc.description.keywords Support vector machine
dc.description.keywords Support vector machine
dc.description.keywords Prior domain knowledge
dc.description.keywords Inductive machine learning
dc.description.keywords Imbalance data
dc.description.keywords Support vector machine
dc.description.keywords Prior domain knowledge
dc.description.keywords Inductive machine learning
dc.description.keywords Imbalance data
dc.description.keywords Support vector machine
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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