Machine Learning Semi-Supervised Algorithms for Gene Selection: A Review

Abstract

Machine learning and data mining have established several effective applications in gene selection analysis. This paper review semi-supervised learning algorithms and gene selection. Semi-Supervised learning is learning that includes experiences that are familiar with the environment because it can deal with labelled and unnamed data. Gene selection is dimension reduction defined as the discovery process of the perfect selection of attributes comprising the whole collected dataset. We review many previous studies on gene selection in semi-supervised learning where each previous research paper tests a group of algorithms to select a gene on a specific set of selected medical data. Each study proposes its algorithm and compares it with previous existing algorithms and compares their accuracy.