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Feature selection and gene clustering from gene expression data

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2 Author(s)
P. Mitra ; Machine Intelligence Unit, Indian Stat. Inst., Kolkata, India ; D. D. Majumder

In This work 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.

Published in:

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on  (Volume:2 )

Date of Conference:

23-26 Aug. 2004