Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification.

Hengyi Zhang

doi:10.3389/fgene.2021.631505

Abstract

Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy.

Highlights

The development of DNA microarray technology has brought about a large number of gene expression data
feature selection algorithm using approximate conditional entropy (FSACE) does not consider the interaction between attributes
Reducing the time complexity of FSACE and seeking more efficient feature selection algorithm considering interaction between attributes are two issues that we will study in the future

Summary

Introduction

The development of DNA microarray technology has brought about a large number of gene expression data. Due to the small sample size and high dimensionality of gene expression data, the traditional classification methods are often ineffective when applied to gene expression data directly (Fu and Wang, 2003; Mitra et al, 2011; Phan et al, 2012; Konstantina et al, 2015). It has become a consensus in the academic community to reduce the dimensionality before classification. Feature selection can reduce the time and space complexity of classification learning algorithm, avoid dimensionality disaster, and improve the prediction accuracy of classification, and help to explain biological phenomena

Methods

Results

Conclusion