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Chaotic binary reptile search algorithm and its feature selection applications

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Abstract

Feature selection (FS) is known as the most challenging problem in the Machine Learning field. FS can be considered an optimization problem that requires an efficient method to prepare its optimal subset of relative features. This article introduces a new FS method-based wrapper scheme that mixes chaotic maps (CMs) and binary Reptile Search Algorithm (RSA) called CRSA, employed to address various FS problems. In this method, different chaotic maps are included with the main ideas of the RSA algorithm. The objective function is revealed to combine three objectives: maximizing the classification accuracy, the number of chosen features, and the complexity of produced wrapper models. To assess the achievement of the proposed methods, 20 UCI datasets are applied, and the results are compared with other well-known methods. The results showed the superiority of the introduced method in bettering other well-known techniques, particularly when applying binary RSA with Tent CM.

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Data is available from the authors upon reasonable request.

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Authors and Affiliations

  1. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan

    Laith Abualigah

  2. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  3. Division of Engineering, New York University Abu Dhabi, Saadiyat Island, 129188, Abu Dhabi, UAE

    Ali Diabat

  4. Department of Civil and Urban Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, 11201, USA

    Ali Diabat

  5. Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan

    Laith Abualigah

  6. School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia

    Laith Abualigah

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  1. Laith Abualigah
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Correspondence to Laith Abualigah.

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Abualigah, L., Diabat, A. Chaotic binary reptile search algorithm and its feature selection applications. J Ambient Intell Human Comput 14, 13931–13947 (2023). https://doi.org/10.1007/s12652-022-04103-5

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  • DOI: https://doi.org/10.1007/s12652-022-04103-5

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