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dc.contributor.authorPrasad, M.en
dc.contributor.authorSowmya, A.en
dc.contributor.authorKoch, I.en
dc.identifier.citationProceedings of the 2004 Intelligent Sensors, Sensor Networks & Information Processing Conference / IEEE: pp.427-432en
dc.description.abstractFeature selection, often used as a pre-processing step to machine learning, is designed to reduce dimensionality, eliminate irrelevant data and improve accuracy. In this paper, we introduce a novel approach to reduce dimensionality of the feature space by employing independent component analysis. While ICA is primarily a feature extraction technique, we use it here as a feature selection technique in a generic way. Our technique, called FSS_ICA, is more efficient than many of its competitors without loss in accuracy. FSS_ICA determines a set of statistically independent features instead of merely reducing the number of the original features. In applications FSS_ICA results in a smaller number of effective features than the relief attribute estimator, and it usually outperforms both the relief attribute estimator and CFS, when used as a pre-processing step for naive Bayes, instance based learning and decision trees. In addition, by disregarding some features, we demonstrate that in some cases FSS_ICA is more accurate than classification based on all features. Also, decision trees built from the pre-processed data are often significantly smaller than those derived from the original feature space. In addition, we also report the performance of ICA on a "real world" application in medical image segmentation.en
dc.description.statementofresponsibilityMithun Prasad, Arcot Sowmya and Inge Kochen
dc.titleEfficient feature selection based on independent component analysisen
dc.typeConference paperen
dc.contributor.conferenceIntelligent Sensors, Sensor Networks and Information Processing Conference (2004 : Melbourne, Victoria)en
pubs.library.collectionMathematical Sciences publicationsen
Appears in Collections:Mathematical Sciences publications

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