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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1-4/2010

01.12.2010 | Original Article

An efficient gene selection technique for cancer recognition based on neighborhood mutual information

verfasst von: Qinghua Hu, Wei Pan, Shuang An, Peijun Ma, Jinmao Wei

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1-4/2010

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Abstract

Gene selection is a key problem in gene expression based cancer recognition and related tasks. A measure, called neighborhood mutual information (NMI), is introduced to evaluate the relevance between genes and related decision in this work. Then the measure is combined with the search strategy of minimal redundancy and maximal relevancy (mRMR) for constructing a NMI based mRMR gene selection algorithm (NMI_mRMR). In addition, it is also found that the first k best genes with respect to NMI are usually enough for cancer classification. We can just perform mRMR on these genes and remove the rest in the preprocessing step, which will lead to reduction of computational time. Based on this observation, an efficient gene selection algorithm, denoted by NMI_EmRMR, is proposed. Several cancer recognition tasks are gathered for testing the proposed technique. The experimental results show NMI_EmRMR is effective and efficient.
Vorheriger Artikel Full-class set classification using the Hungarian algorithm
Nächster Artikel Genetic Algorithm-Neural Network (GANN): a study of neural network activation functions and depth of genetic algorithm search applied to feature selection

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Metadaten
Titel
An efficient gene selection technique for cancer recognition based on neighborhood mutual information
verfasst von
Qinghua Hu
Wei Pan
Shuang An
Peijun Ma
Jinmao Wei
Publikationsdatum
01.12.2010
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1-4/2010
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-010-0008-6

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