General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification
Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-02-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1861407 |
| _version_ | 1797684873840820224 |
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| author | Jingwei Too Seyedali Mirjalili |
| author_facet | Jingwei Too Seyedali Mirjalili |
| author_sort | Jingwei Too |
| collection | DOAJ |
| description | Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms. |
| first_indexed | 2024-03-12T00:36:02Z |
| format | Article |
| id | doaj.art-47ff7c16f348476083d3231044e81670 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:02Z |
| publishDate | 2021-02-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-47ff7c16f348476083d3231044e816702023-09-15T09:33:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-02-0135324726310.1080/08839514.2020.18614071861407General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data ClassificationJingwei Too0Seyedali Mirjalili1Universiti Teknikal Malaysia MelakaTorrens University AustraliaFinding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms.http://dx.doi.org/10.1080/08839514.2020.1861407 |
| spellingShingle | Jingwei Too Seyedali Mirjalili General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification Applied Artificial Intelligence |
| title | General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification |
| title_full | General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification |
| title_fullStr | General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification |
| title_full_unstemmed | General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification |
| title_short | General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification |
| title_sort | general learning equilibrium optimizer a new feature selection method for biological data classification |
| url | http://dx.doi.org/10.1080/08839514.2020.1861407 |
| work_keys_str_mv | AT jingweitoo generallearningequilibriumoptimizeranewfeatureselectionmethodforbiologicaldataclassification AT seyedalimirjalili generallearningequilibriumoptimizeranewfeatureselectionmethodforbiologicaldataclassification |