期刊
GENOMICS
卷 110, 期 1, 页码 10-17出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ygeno.2017.07.010
关键词
Cancer classification; Gene selection; Bat algorithm; Feature selection; Evolutionary algorithms; Microarray data analysis
Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.
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