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Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data

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Abstract

High-dimensional microarray data suffer from the confounding effects of irrelevant, redundant and noisy genes on the scalability and efficiency of classification algorithms. In order for an effective dimensionality reduction and the selection of informative genes, this paper introduces a novel approach using fractional calculus concepts. This study proposes a modified version of binary the bat algorithm named fractional-order binary bat algorithm (FBBA) able to control the convergence process using more historical memory of bat behaviors. The gene selection technique contains a two-stage hybrid filter/wrapper method which employs a new correlation-based feature clustering (CFC) algorithm in the filter stage and the FBBA in the wrapper stage. The CFC-FBBA is evaluated on ten microarray gene expression datasets by employing the support vector machine classifier with a k-fold Monte Carlo cross validation data partitioning model. Furthermore, the results show that the CFC-FBBA, while minimizing the size of the gene subset, achieves the highest classification accuracy in most cases compared to several state-of-art hybrid techniques.

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Authors and Affiliations

  1. Department of Computer and Mechatronics Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Ahmad Esfandiari, Faezeh Farivar & Hamid Khaloozadeh

  2. Department of Systems and Control, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Seyed-Khandan, Shariati Ave., Tehran, 16317-14191, Iran

    Hamid Khaloozadeh

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  1. Ahmad Esfandiari
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  2. Faezeh Farivar
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Correspondence to Hamid Khaloozadeh.

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Esfandiari, A., Farivar, F. & Khaloozadeh, H. Fractional-order binary bat algorithm for feature selection on high-dimensional microarray data. J Ambient Intell Human Comput 14, 7453–7467 (2023). https://doi.org/10.1007/s12652-022-04450-3

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