Boosted sooty tern optimization algorithm for global optimization and feature selection

Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and...

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Veröffentlicht in:Expert systems with applications Jg. 213; S. 119015
Hauptverfasser: Houssein, Essam H., Oliva, Diego, Çelik, Emre, Emam, Marwa M., Ghoniem, Rania M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2023
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ISSN:0957-4174, 1873-6793
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Abstract Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. •An enhanced algorithm called the mSTOA that employs three strategies is proposed.•mSTOA efficiency and performance are verified on several benchmarks.•CEC’20 test suite are used for algorithm validation.•mSTOA is proposed as an alternate feature selection approach.
AbstractList Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting prominent features and eliminating redundant and irrelevant data to classify results better. The goals of FS comprise dimensionality reduction and enhancing the classification accuracy in general, accompanied by great significance in different fields like data mining applications, pattern classification, and data analysis. Using powerful optimization algorithms is crucial to obtaining the best subsets of information in FS. Different metaheuristics, such as the Sooty Tern Optimization Algorithm (STOA), help to optimize the FS problem. However, such kind of techniques tends to converge in sub-optimal solutions. To overcome this problem in the STOA, an improved version called mSTOA is introduced. It employs the balancing exploration/exploitation strategy, self-adaptive of the control parameters strategy, and population reduction strategy. The proposed approach is proposed for solving the FS problem, but also it has been validated over benchmark optimization problems from the CEC 2020. To assess the performance of the mSTOA, it has also been tested with different algorithms. The experiments in terms of FS provide qualitative and quantitative evidence of the capabilities of the mSTOA for extracting the optimal subset of features. Besides, statistical analyses and no-parametric tests were also conducted to validate the result obtained by the mSTOA in optimization. •An enhanced algorithm called the mSTOA that employs three strategies is proposed.•mSTOA efficiency and performance are verified on several benchmarks.•CEC’20 test suite are used for algorithm validation.•mSTOA is proposed as an alternate feature selection approach.
ArticleNumber 119015
Author Çelik, Emre
Ghoniem, Rania M.
Oliva, Diego
Houssein, Essam H.
Emam, Marwa M.
Author_xml – sequence: 1
  givenname: Essam H.
  orcidid: 0000-0002-8127-7233
  surname: Houssein
  fullname: Houssein, Essam H.
  email: essam.halim@mu.edu.eg
  organization: Faculty of Computers and Information, Minia University, Minia, Egypt
– sequence: 2
  givenname: Diego
  orcidid: 0000-0001-8781-7993
  surname: Oliva
  fullname: Oliva, Diego
  email: diego.oliva@cucei.udg.mx
  organization: Depto. Innovación Basada en la Información y el Conocimiento, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico
– sequence: 3
  givenname: Emre
  orcidid: 0000-0002-2961-0035
  surname: Çelik
  fullname: Çelik, Emre
  email: emrecelik@duzce.edu.tr
  organization: Department of Electrical and Electronics Engineering, Faculty of Engineering, Düzce University, Düzce, Turkey
– sequence: 4
  givenname: Marwa M.
  orcidid: 0000-0001-7399-6839
  surname: Emam
  fullname: Emam, Marwa M.
  email: marwa.khalef@mu.edu.eg
  organization: Faculty of Computers and Information, Minia University, Minia, Egypt
– sequence: 5
  givenname: Rania M.
  orcidid: 0000-0002-7740-7402
  surname: Ghoniem
  fullname: Ghoniem, Rania M.
  email: RMGhoniem@pnu.edu.sa
  organization: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Keywords Dimensionality reduction
Sooty Tern Optimization Algorithm (STOA)
Feature selection
Optimization algorithm
Metaheuristics
Language English
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Snippet Feature selection (FS) represents an optimization problem that aims to simplify and improve the quality of highly dimensional datasets through selecting...
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StartPage 119015
SubjectTerms Dimensionality reduction
Feature selection
Metaheuristics
Optimization algorithm
Sooty Tern Optimization Algorithm (STOA)
Title Boosted sooty tern optimization algorithm for global optimization and feature selection
URI https://dx.doi.org/10.1016/j.eswa.2022.119015
Volume 213
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