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https://hdl.handle.net/2440/55894
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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 Responsibility: | 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. |
DOI: | 10.1109/ISSNIP.2004.1417499 |
Published version: | http://dx.doi.org/10.1109/issnip.2004.1417499 |
Appears in Collections: | Aurora harvest Mathematical Sciences publications |
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