Binary grasshopper optimisation algorithm approaches for feature selection problems
•Three binary versions of Grasshopper Optimization Algorithm (BGOA) are proposed.•Wrapper-based feature selection techniques are proposed using the BGOA algorithms.•The proposed algorithms are benchmarked on 18 standard UCI datasets.•The results are compared with 10 algorithms.•The results show the...
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| Published in: | Expert systems with applications Vol. 117; pp. 267 - 286 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Ltd
01.03.2019
Elsevier BV |
| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •Three binary versions of Grasshopper Optimization Algorithm (BGOA) are proposed.•Wrapper-based feature selection techniques are proposed using the BGOA algorithms.•The proposed algorithms are benchmarked on 18 standard UCI datasets.•The results are compared with 10 algorithms.•The results show the merits of the proposed algorithms and feature selection methods.
Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. |
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| AbstractList | Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. •Three binary versions of Grasshopper Optimization Algorithm (BGOA) are proposed.•Wrapper-based feature selection techniques are proposed using the BGOA algorithms.•The proposed algorithms are benchmarked on 18 standard UCI datasets.•The results are compared with 10 algorithms.•The results show the merits of the proposed algorithms and feature selection methods. Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. |
| Author | Mafarja, Majdi Faris, Hossam Aljarah, Ibrahim Al-Zoubi, Ala’ M. Hammouri, Abdelaziz I. Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Majdi orcidid: 0000-0002-0387-8252 surname: Mafarja fullname: Mafarja, Majdi email: mmafarja@birzeit.edu, mmafarjeh@gmail.com organization: Department of Computer Science, Birzeit University, Birzeit, Palestine – sequence: 2 givenname: Ibrahim orcidid: 0000-0002-9265-9819 surname: Aljarah fullname: Aljarah, Ibrahim email: i.aljarah@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 3 givenname: Hossam orcidid: 0000-0003-4261-8127 surname: Faris fullname: Faris, Hossam email: hossam.faris@ju.edu.jo organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 4 givenname: Abdelaziz I. orcidid: 0000-0002-0612-8246 surname: Hammouri fullname: Hammouri, Abdelaziz I. email: Aziz@bau.edu.jo organization: Department of Computer Information Systems, Al-Balqa Applied University, Al-Salt, Jordan – sequence: 5 givenname: Ala’ M. orcidid: 0000-0003-0414-3570 surname: Al-Zoubi fullname: Al-Zoubi, Ala’ M. email: alaah14@gmail.com organization: King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan – sequence: 6 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali email: seyedali.mirjalili@griffithuni.edu.au organization: Institute for Integrated and Intelligent Systems, Griffith University, Nathan, Brisbane, QLD 4111, Australia |
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| Copyright | 2018 Elsevier Ltd Copyright Elsevier BV Mar 2019 |
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| Title | Binary grasshopper optimisation algorithm approaches for feature selection problems |
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