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A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques

Author

Listed:
  • Lichao Sun

    (Computer School, Yangtze University, Jingzhou 434023, China)

  • Hang Qin

    (Computer School, Yangtze University, Jingzhou 434023, China)

  • Krzysztof Przystupa

    (Department Automation, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
    The State School of Higher Education, Pocztowa 54, 22-100 Chełm, Poland)

  • Yanrong Cui

    (Computer School, Yangtze University, Jingzhou 434023, China)

  • Orest Kochan

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China
    Department of Measuring Information Technologies, Institute of Computer Technologies, Automation and Metrology, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Mikołaj Skowron

    (Department of Electrical and Power Engineering, AGH University of Science and Technology, A. Mickiewicza 30, 30-059 Krakow, Poland)

  • Jun Su

    (School of Computer Science, Hubei University of Technology, Wuhan 430068, China)

Abstract

Feature selection is the procedure of extracting the optimal subset of features from an elementary feature set, to reduce the dimensionality of the data. It is an important part of improving the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of the most popular methods for dealing with optimization issues. This article proposes a novel feature selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA). Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for feature selection. In addition, a multi-level adjustment factor strategy is adopted to obtain an equilibrium between exploration and exploitation. The performance of MetaSCA was assessed using the following evaluation indicators: average fitness, worst fitness, optimal fitness, classification accuracy, average proportion of optimal feature subsets, feature selection time, and standard deviation. The performance was measured on the UCI data set and then compared with three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale optimization algorithm (WOA). It was demonstrated by the simulation data results that the MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the UCI data sets, in most of the cases.

Suggested Citation

  • Lichao Sun & Hang Qin & Krzysztof Przystupa & Yanrong Cui & Orest Kochan & Mikołaj Skowron & Jun Su, 2022. "A Hybrid Feature Selection Framework Using Improved Sine Cosine Algorithm with Metaheuristic Techniques," Energies, MDPI, vol. 15(10), pages 1-24, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3485-:d:812289
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    References listed on IDEAS

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    1. Igor Korobiichuk & Viktorij Mel’nick & Vladyslav Shybetskyi & Sergii Kostyk & Myroslava Kalinina, 2022. "Optimization of Heat Exchange Plate Geometry by Modeling Physical Processes Using CAD," Energies, MDPI, vol. 15(4), pages 1-18, February.
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