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Type: Conference paper
Title: Efficient feature selection based on independent component analysis
Author: Prasad, M.
Sowmya, A.
Koch, I.
Citation: Proceedings of the 2004 Intelligent Sensors, Sensor Networks & Information Processing Conference / IEEE: pp.427-432
Publisher: IEEE
Publisher Place: on-line
Issue Date: 2004
ISBN: 0780388941
Conference Name: Intelligent Sensors, Sensor Networks and Information Processing Conference (2004 : Melbourne, Victoria)
Statement of
Mithun Prasad, Arcot Sowmya and Inge Koch
Abstract: Feature 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.
RMID: 0020094320
DOI: 10.1109/ISSNIP.2004.1417499
Appears in Collections:Mathematical Sciences publications

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