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17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
Feature Selection and Gene Clustering from Gene Expression Data
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
Pabitra Mitra, Indian Statistical Institute
Dwijesh Dutta Majumder, Indian Statistical Institute
In this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. The method is based on measuring similarity between features/genes whereby redundancy therein is removed. This does not need any search and therefore is fast. A novel feature similarity measure, called maximum information compression index, is used. The feature selection algorithm also obtains gene clusters in a multiscale fashion. The superiority of the algorithm, in terms of speed and performance, is established on a real life molecular cancer classification dataset.
Index Terms:
Microarray, maximal information compression index, cancer classification, representation entropy, data mining
Citation:
Pabitra Mitra, Dwijesh Dutta Majumder, "Feature Selection and Gene Clustering from Gene Expression Data," icpr, vol. 2, pp.343-346, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 2, 2004
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