Nur fatmawati
16611047
An improved minimum redundancy maximum relevance approach for feature selection in gene expression data.
Objective is to find such a feature set for which the
mutual information among the features and the class labels are maximized and
the mutual information among the features are minimized. Therefore, the goal of
the proposed method is to find the most relevant and least redundant feature
set. each iteration a non-dominated feature set with respect to relevance and
redundancy is generated and from this set of features, the most relevant and
non-redundant feature is included in the final feature set. on microarray
gene expression data to find the most relevant and non-redundant genes and the
performance of the proposed method is compared with that of the popular mRMR
(MIQ) and mRMR (MID) For measuring
the relevance and redundancy of a feature or gene, the mutual information has
been considered.The performance of
technique is evaluated based on some real-life microarray gene
expression datasets for selecting non-redundant and relevant genes.
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