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Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data

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Abstract

Feature selection is a very critical component in the workflow of biomedical data mining applications. In particular, there is a need for feature selection methods that can find complex relationships among genes, yet computationally efficient. Within the scope of microarray data analysis, the genetic bee colony (Gbc) algorithm is one of the best feature selection algorithms, which leverages the combination between genetic and ant colony optimization algorithms to search for the optimal solution. In this paper, we analyse in depth the fundamentals lying behind the Gbc and propose some improvements in both efficiency and accuracy, so that researchers can even take more advantage of this excellent method. By (i) replacing the filtering phase of Gbc with a more efficient technique, (ii) improving the population generation in the artificial colony algorithm used in Gbc, and (iii) improving the exploitation method in Gbc, our experiments in microarray data sets reveal that our new method Gbc+ is not only significantly more accurate, but also around ten times faster on average than the original

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Acknowledgements

This work was partially supported by the Grant-in-Aid for Scientific Research (JSPS KAKENHI Grant Number 17H00762) from the Japan Society for the Promotion of Science.

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Authors and Affiliations

  1. Graduate School of Applied Informatics, University of Hyogo, Kobe, 651-2197, Japan

    Adrian Pino Angulo & Kilho Shin

  2. Grupo de Procesamiento de Datos Biomédicos, Universidad de Holguín, Holguín, Cuba

    Camilo Velázquez-Rodríguez

Authors
  1. Adrian Pino Angulo
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  2. Kilho Shin
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  3. Camilo Velázquez-Rodríguez
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Correspondence to Adrian Pino Angulo.

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Pino Angulo, A., Shin, K. & Velázquez-Rodríguez, C. Improving the genetic bee colony optimization algorithm for efficient gene selection in microarray data. Prog Artif Intell 7, 399–410 (2018). https://doi.org/10.1007/s13748-018-0161-9

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  • DOI: https://doi.org/10.1007/s13748-018-0161-9

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