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

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dc.contributor.author Yu, Ting en_US
dc.contributor.author Simoff, Simeon en_US
dc.contributor.author Jan, Tony en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:24:40Z
dc.date.available 2011-02-07T06:24:40Z
dc.date.issued 2010 en_US
dc.identifier 2009007499 en_US
dc.identifier.citation Yu Ting, Simoff Simeon, and Jan Tony 2010, 'VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning', Elsevier Science B.V, vol. 73, no. 13-15, pp. 2614-2623. en_US
dc.identifier.issn 0925-2312 en_US
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 methoda??VQSVMa??extends 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. en_US
dc.language en_US
dc.publisher Elsevier Science B.V en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.neucom.2010.05.007 en_US
dc.title VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning en_US
dc.parent Neurocomputing en_US
dc.journal.volume 73 en_US
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 en_US
dc.personcode 10090254 en_US
dc.personcode 000716 en_US
dc.personcode 020524 en_US
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.staffid 020524 en_US

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