Abstract
Gene selection algorithm in micro-array data classification problem finds a small set of genes which are most informative and distinctive. A well-performed gene selection algorithm should pick a set of genes that achieve high performance and the size of this gene set should be as small as possible. Many of the existing gene selection algorithms suffer from either low performance or large size. In this study, we propose a wrapper gene selection approach, named WERFE, within a recursive feature elimination (RFE) framework to make the classification more efficient. This WERFE employs an ensemble strategy, takes advantages of a variety of gene selection methods and assembles the top selected genes in each approach as the final gene subset. By integrating multiple gene selection algorithms, the optimal gene subset is determined through prioritizing the more important genes selected by each gene selection method and a more discriminative and compact gene subset can be selected. Experimental results show that the proposed method can achieve state-of-the-art performance.
Highlights
Gene expression data contains gene activity information, and it reflects the current physiological state of the cell, for example, whether the drug is effective on the cell, etc
Feature selection algorithm plays as an integral part of the learning algorithm, and the classification output is used to evaluate the importance of the feature subsets
The proposed Wrapper Embedded Recursive Feature Elimination (WERFE) can ensemble any number of gene selection algorithms
Summary
Gene expression data contains gene activity information, and it reflects the current physiological state of the cell, for example, whether the drug is effective on the cell, etc. It plays important roles in clinical diagnosis and drug efficacy judgment, such as assisting diagnosis and revealing disease occurrence mechanism (Lambrou et al, 2019). Since the dimensionality of gene expression data is often up to tens of thousands, it often consumes huge amount of time for analysis and it is difficult to make full use of it. Before the analysis of gene expression data, gene selection, which aims to reduce the dimensionality, is always carried out as the first step
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