SVM-RFE With MRMR Filter for Gene Selection

By Piyushkumar A. Mundra, Jagath Rajapakse. IEEE Transactions on Nanobioscience, 2010

327 citations recorded by the source corpus.

We enhance the support vector machine recursive feature elimination (SVM-RFE) method for gene selection by incorporating a minimum-redundancy maximum-relevancy (MRMR) filter. The relevancy of a set of genes are measured by the mutual information among genes and class labels, and the redundancy is given by the mutual information among the genes. The method improved identification of cancer tissues from benign tissues on several benchmark datasets, as it takes into account the redundancy among the genes during their selection. The method selected a less number of genes compared to MRMR or SVM-RFE on most datasets. Gene ontology analyses revealed that the method selected genes that are relevant for distinguishing cancerous samples and have similar functional properties. The method provides a framework for combining filter methods and wrapper methods of gene selection, as illustrated with MRMR and SVM-RFE methods.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e637ed6d0df9f99867621012c0ba493f1925deaa

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Citations in the corpus

A review of microarray datasets and applied feature selection methods (2014, methodology): “Bootstrap sampling was less used [113,85,124], probably due to its high computational cost, and there are also some representatives of leave-one-out cross-validation [33,73,92].”
From explanations to feature selection: assessing SHAP values as feature selection mechanism (2020, methodology): “rank features and applied to SVM classifier [30] to perform Recursive Feature Selection.”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, methodology): “For example, Hu et al. [13] employed the search strategy of mRMR for constructing neighbourhood mutual information (NMI) for improving the efficiency of mRMR gene selection, Akadi et al. [14] proposed a two-stage gene selection by combining mRMR as filter and genetic algorithm as wrapper , Mundra an”