An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data

TL;DR: The efficiency and effectiveness of the method were demonstrated through comparisons with other feature selection techniques, and the results show that the method outperformed other methods published in the literature.

Abstract: Motivation: Gene selection for cancer classification is one of the most important topics in the biomedical field However, microarray data pose a severe challenge for computational techniques We need dimension reduction techniques that identify a small set of genes to achieve better learning performance From the perspective of machine learning, the selection of genes can be considered to be a feature selection problem that aims to find a small subset of features that has the most discriminative information for the target Results: In this article, we proposed an Ensemble Correlation-Based Gene Selection algorithm based on symmetrical uncertainty and Support Vector Machine In our method, symmetrical uncertainty was used to analyze the relevance of the genes, the different starting points of the relevant subset were used to generate the gene subsets and the Support Vector Machine was used as an evaluation criterion of the wrapper The efficiency and effectiveness of our method were demonstrated through comparisons with other feature selection techniques, and the results show that our method outperformed other methods published in the literature Availability: By request from the author Contact:pyz@dblabchungbukackr; khryu@dblabcbnuackr