Paper Info
Title
Feature clustering and feature discretization assisting gene selection for molecular classification using fuzzy c-means and expectation–maximization algorithm
Abstract
In this paper, a novel gene selection benefiting from feature clustering and feature discretization is developed. In large numbers of genes, unsupervised fuzzy clustering algorithm facilitates the analysis of both similarities and dissimilarities. The supervised process, adopting information gain and statistical Chi-square test, is applied to approve the relevant gene clusters. Then, expectation–maximization algorithm discretizes the candidate genes and helps to recognize distinguishability. In our previously proposed selection criterion, we finalized gene selection and generated the gene subsets for molecular classification. For high-dimensional datasets congested with erroneous or ambiguous information, the current scheme is particularly suitable in its own right. The efficiency and effectiveness are verified by our experimental results.
Year
DOI
Venue
2021
10.1007/s11227-020-03480-y
The Journal of Supercomputing
Keywords
DocType
Volume
Feature clustering, Feature discretization, Gene selection, Fuzzy cluster analysis, Expectation–maximization algorithm
Journal
77
Issue
ISSN
Citations 
6
0920-8542
0
PageRank 
References 
Authors
0.34
27
1
Name
Order
Citations
PageRank
Hung-Yi Lin1398.74