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

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Abstract

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.

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Notes

  1. It is available: http://csse.szu.edu.cn/staff/zhuzx/Datasets.html.

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Acknowledgements

Feature selection for microarray data classification This work was supported by National Natural Science Foundation of China (Nos. 61802114, 61802113, 61602156S, 61972134), Science and Technology Development Plan Project of Henan Province (No. 202102210173) and Scientific Research Foundation of the Higher Education Institutions of Henan Province (18A520021). The manuscript is selected and recommended by CBC 2019 (The Fourth CCF Bioinformatics Conference).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Kaifeng, China

    Ge Zhang, Jincui Hou, Jianlin Wang & Chaokun Yan

  2. Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, 475004, China

    Ge Zhang, Jianlin Wang & Chaokun Yan

  3. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China

    Junwei Luo

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Correspondence to Chaokun Yan or Junwei Luo.

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Zhang, G., Hou, J., Wang, J. et al. Feature Selection for Microarray Data Classification Using Hybrid Information Gain and a Modified Binary Krill Herd Algorithm. Interdiscip Sci Comput Life Sci 12, 288–301 (2020). https://doi.org/10.1007/s12539-020-00372-w

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