A Comprehensive Survey of Recent Hybrid Feature Selection Methods in Cancer Microarray Gene Expression Data

Abstract

In the diagnosis and treatment of cancer, cancer classification is a vital issue. Gene selection is much needed to solve the high dimensionality issue in microarray data, small sample size, and noisy. The best way to classify cancer is to select those genes that hold the most informative ones, and this process contributes significantly to the classification performance of microarrays. In this survey, we comprehensively studied hybrid selection methods proposed since 2017, that may be used for comparison to several other algorithms proposed for gene selection in cancer classification in the past and looked to see if there are any challenges future authors that need to be discussed.