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Feature Selection in Machine Learning by Hybrid Sine Cosine Metaheuristics

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

Feature selection problem from the domain of machine learning refers to selecting only those features from the high dimensional datasets, that have prominent influence on dependent variable(s). In this way, dataset dimensionallity is reduced and only the riches data is kept, training process of machine learning model becomes more efficient and accuracy is increased. This manuscript proposes a new hybridized version of the sine cosine algorithm adjusting for solving feature selection problem. Hybridization is relatively novel approach for combing and improving metaheuristics optimizer. Notwithstanding that the basic sine cosine algorithm establishes good performance for solving NP hard challenges, based on simulation results, it was concluded that there is still space for improvement in its exploitation process. Original sine cosine algorithm and proposed hybridized implementation were tested on a well-known 21 machine learning datasets retrieved from the UCL repository. Comparative analysis between hybrid sine cosine and original one, as well as with 10 other state-of-the-art metaheuristics was conducted. Established results in terms of classification accuracy and fitness prove the robustness and efficiency of proposed method for solving this type of NP hard challenge.

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Acknowledgment

The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Nebojsa Bacanin, Aleksandar Petrovic, Miodrag Zivkovic, Timea Bezdan & Milos Antonijevic

Authors
  1. Nebojsa Bacanin
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  2. Aleksandar Petrovic
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  3. Miodrag Zivkovic
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  4. Timea Bezdan
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  5. Milos Antonijevic
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Correspondence to Nebojsa Bacanin .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Bacanin, N., Petrovic, A., Zivkovic, M., Bezdan, T., Antonijevic, M. (2021). Feature Selection in Machine Learning by Hybrid Sine Cosine Metaheuristics. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_53

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81461-8

  • Online ISBN: 978-3-030-81462-5

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