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The new technique, called feature selection based on independent component analysis (FS_ICA), efficiently builds a reduced set of features without loss in accuracy and also has a fast incremental version. When used as a first step in supervised learning, FS_ICA outperforms comparable methods in efficiency without loss of classification accuracy. For large data sets as in medical image segmentation of high-resolution computer tomography images, FS_ICA reduces dimensionality of the data set substantially and results in efficient and accurate classification. <\/jats:p>","DOI":"10.1142\/s1469026808002387","type":"journal-article","created":{"date-parts":[[2009,4,3]],"date-time":"2009-04-03T08:05:00Z","timestamp":1238745900000},"page":"447-468","source":"Crossref","is-referenced-by-count":4,"title":["DESIGNING RELEVANT FEATURES FOR CONTINUOUS DATA SETS USING ICA"],"prefix":"10.1142","volume":"07","author":[{"given":"MITHUN","family":"PRASAD","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia"}]},{"given":"ARCOT","family":"SOWMYA","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia"}]},{"given":"INGE","family":"KOCH","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","first-page":"1","volume":"3","author":"Bach F. 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