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Mutual information rough sets feature selection and classification for microarray data analysis
Author(s)
Ounsrimuang, Pimolrat
Boonjing, Veera
Date Issued
January 1, 2014
Type
Article
Abstract
The feature selection (FS) techniques aim to reduce the subset size of an original data set, which are retained in the most useful information by selecting the most informative feature instead of irrelevant or redundant features. The benefits of FS for classification analysis can reduce the input data, improved predictive accuracy, learned knowledge is that easily understood, and reduced execution time. Many approaches based on rough set theory up to now, have operated the dependency function for measuring the goodness of the feature. However, there is not tolerance to noisy or inconsistency data, especially on high dimensional data microarray data sets. Moreover, mostly relevant information could be invisible by using only information from a positive region but neglecting a boundary region, mostly relevant may be invisible. Therefore, this paper proposes the maximal positive region and minimal boundary region criterion, based on rough set and mutual information, which use the different values among the information contained in the positive region, and the information contained in the boundary region. The experimental results indicate that our proposed method can increase the classification accuracy. © 2014 Pushpa Publishing House, Allahabad, India.
Citation
Far East Journal of Mathematical Sciences, 85(2), 129-149, 2014