Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be...

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Vydáno v:	IEEE access Ročník 11; s. 79750 - 79776
Hlavní autoři:	Abdelhamid, Abdelaziz A., El-Kenawy, El-Sayed M., Ibrahim, Abdelhameed, Eid, Marwa Metwally, Khafaga, Doaa Sami, Alhussan, Amel Ali, Mirjalili, Seyedali, Khodadadi, Nima, Lim, Wei Hong, Shams, Mahmoud Y.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Classification algorithms Data mining dipper throated optimization algorithm Feature extraction Feature selection Genetic algorithms Heuristic methods K-nearest neighbors algorithm Machine learning Mathematical models meta-heuristic optimization Metaheuristics Optimization Optimization algorithms Particle swarm optimization Pattern recognition Sine cosine optimization algorithm Stability analysis Trigonometric functions
ISSN:	2169-3536, 2169-3536
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Abstract	Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features' effectiveness in classification problems. Results: The achieved results demonstrate the algorithm's superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Multiverse Optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic Algorithm (GA), the hybrid of GWO and GA, and Firefly Algorithm (FA). Moreover, Wilcoxon's rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method's significance, superiority, and statistical difference.
AbstractList	Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features’ effectiveness in classification problems. Results: The achieved results demonstrate the algorithm’s superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Multiverse Optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic Algorithm (GA), the hybrid of GWO and GA, and Firefly Algorithm (FA). Moreover, Wilcoxon’s rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method’s significance, superiority, and statistical difference.
Author	Alhussan, Amel Ali Ibrahim, Abdelhameed Lim, Wei Hong Shams, Mahmoud Y. Abdelhamid, Abdelaziz A. Khafaga, Doaa Sami Khodadadi, Nima El-Kenawy, El-Sayed M. Mirjalili, Seyedali Eid, Marwa Metwally
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SubjectTerms	Classification algorithms Data mining dipper throated optimization algorithm Feature extraction Feature selection Genetic algorithms Heuristic methods K-nearest neighbors algorithm Machine learning Mathematical models meta-heuristic optimization Metaheuristics Optimization Optimization algorithms Particle swarm optimization Pattern recognition Sine cosine optimization algorithm Stability analysis Trigonometric functions
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Title	Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms
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