BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems

Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel fe...

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Vydané v:Applied sciences Ročník 11; číslo 14; s. 6516
Hlavní autori: Chantar, Hamouda, Thaher, Thaer, Turabieh, Hamza, Mafarja, Majdi, Sheta, Alaa
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.07.2021
Predmet:
Algorithms
Classification
Data mining
Exploitation
feature selection
Harris Hawks Optimizer
Heuristic
HHO
Machine learning
metaheuristics
Optimization
time-varying
transfer functions
ISSN:2076-3417, 2076-3417
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Shrnutí:Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app11146516