Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm

• Published in: Interdisciplinary sciences : computational life sciences Vol. 12; no. 3; pp. 288 - 301
• Main Authors: Zhang, Ge, Hou, Jincui, Wang, Jianlin, Yan, Chaokun, Luo, Junwei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Datasets; Feature selection; Health Sciences; Hyperbolic functions; Immunoglobulins; Krill; Life Sciences; Mathematical and Computational Physics; Medicine; Original Research Article; Searching; Statistics for Life Sciences; Strategy; Theoretical; Theoretical and Computational Chemistry; Transfer factor; Feature selection; Information gain; Modified binary krill herd algorithm; Microarray datasets
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-020-00372-w

Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose, we propose a novel feature selection strategy in this paper, called IG-MBKH. A pre-screening method of feature ranking which is based on information gain (IG) and an improved binary krill herd (MBKH) algorithm are integrated in this strategy. When searching for feature subsets using MBKH, a hyperbolic tangent function, an adaptive transfer factor, and a chaos memory weight factor are introduced to facilitate a better searching the possible feature subsets. The results indicates that the IG-MBKH algorithm can achieve improvement in convergence, the number of features and classification accuracy when compared to the BKH, MBKH, and several newest algorithms. Furthermore, we evaluate the impact of different classifiers on the performance of the strategy we propose.