Feature selection techniques for microarray dataset: a review

Abstract

Automatic speech recognition (ASR) approach is dependent on optimal for many researchers working on feature selection (FS) techniques, finding an appropriate feature from the microarray dataset has turned into a bottleneck. Researchers often create FS approaches and algorithms with the goal of improving accuracy in microarray datasets. The main goal of this study is to present a variety of contemporary FS techniques, such as filter, wrapper, and embedded methods proposed for microarray datasets to work on multi-class classification problems and different approaches to enhance the performance of learning algorithms, to address the imbalance issue in the data set, and to support research efforts on microarray dataset. This study is based on critical review questions (CRQ) constructed using feature election methods described in the review methodology and applied to a microarray dataset. We discussed the analysed findings and future prospects of FS strategies for multi-class classification issues using microarray datasets, as well as prospective ways to speed up computing environment.