Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification
Abstract Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The Chimp Optimization Algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence ra...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-57518-9 |
| _version_ | 1797247337351872512 |
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| author | Li Zhang XiaoBo Chen |
| author_facet | Li Zhang XiaoBo Chen |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | Abstract Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The Chimp Optimization Algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence rate. However, CHoA has a weak exploration capability and tends to fall into local optimal solutions in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. To solve this problem, this paper proposes the Enhanced Chimp Hierarchy Optimization Algorithm for adaptive lens imaging (ALI-CHoASH) for searching the optimal classification problems for the optimal subset of features. Specifically, to enhance the exploration and exploitation capability of CHoA, we designed a chimp social hierarchy. We employed a novel social class factor to label the class situation of each chimp, enabling effective modelling and optimization of the relationships among chimp individuals. Then, to parse chimps’ social and collaborative behaviours with different social classes, we introduce other attacking prey and autonomous search strategies to help chimp individuals approach the optimal solution faster. In addition, considering the poor diversity of chimp groups in the late iteration, we propose an adaptive lens imaging back-learning strategy to avoid the algorithm falling into a local optimum. Finally, we validate the improvement of ALI-CHoASH in exploration and exploitation capabilities using several high-dimensional datasets. We also compare ALI-CHoASH with eight state-of-the-art methods in classification accuracy, feature subset size, and computation time to demonstrate its superiority. |
| first_indexed | 2024-04-24T19:57:05Z |
| format | Article |
| id | doaj.art-6be45ffc28bc43579422e46f78d6a97a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:57:05Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6be45ffc28bc43579422e46f78d6a97a2024-03-24T12:16:32ZengNature PortfolioScientific Reports2045-23222024-03-0114113210.1038/s41598-024-57518-9Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classificationLi Zhang0XiaoBo Chen1College of Computer Engineering, Jiangsu University of TechnologyKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityAbstract Feature selection is a critical component of machine learning and data mining to remove redundant and irrelevant features from a dataset. The Chimp Optimization Algorithm (CHoA) is widely applicable to various optimization problems due to its low number of parameters and fast convergence rate. However, CHoA has a weak exploration capability and tends to fall into local optimal solutions in solving the feature selection process, leading to ineffective removal of irrelevant and redundant features. To solve this problem, this paper proposes the Enhanced Chimp Hierarchy Optimization Algorithm for adaptive lens imaging (ALI-CHoASH) for searching the optimal classification problems for the optimal subset of features. Specifically, to enhance the exploration and exploitation capability of CHoA, we designed a chimp social hierarchy. We employed a novel social class factor to label the class situation of each chimp, enabling effective modelling and optimization of the relationships among chimp individuals. Then, to parse chimps’ social and collaborative behaviours with different social classes, we introduce other attacking prey and autonomous search strategies to help chimp individuals approach the optimal solution faster. In addition, considering the poor diversity of chimp groups in the late iteration, we propose an adaptive lens imaging back-learning strategy to avoid the algorithm falling into a local optimum. Finally, we validate the improvement of ALI-CHoASH in exploration and exploitation capabilities using several high-dimensional datasets. We also compare ALI-CHoASH with eight state-of-the-art methods in classification accuracy, feature subset size, and computation time to demonstrate its superiority.https://doi.org/10.1038/s41598-024-57518-9Feature selectionChimp optimization algorithmHierarchySocial class factorLocal optimal |
| spellingShingle | Li Zhang XiaoBo Chen Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification Scientific Reports Feature selection Chimp optimization algorithm Hierarchy Social class factor Local optimal |
| title | Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| title_full | Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| title_fullStr | Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| title_full_unstemmed | Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| title_short | Enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| title_sort | enhanced chimp hierarchy optimization algorithm with adaptive lens imaging for feature selection in data classification |
| topic | Feature selection Chimp optimization algorithm Hierarchy Social class factor Local optimal |
| url | https://doi.org/10.1038/s41598-024-57518-9 |
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