Feature selection techniques for microarray datasets: a comprehensive review, taxonomy, and future directions

For optimal results, retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection (FS) techniques. The aim of this review is to provide a thorough description of various, recent FS techniques. This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation, in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.