Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification - Zhang, Hengyi (Frontiers in Genetics, 2021)
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 paper, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved using the monotonicity of entropy. Firstly, the fuzzy relation matrix is established using 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 information and algebra. Finally, the greedy algorithm based on the approximate conditional entropy is used to select features. Experimental results for several gene data sets show that our algorithm is superior to some state-of-the-art algorithms in terms of classification accuracy.