Time-varying hierarchical chains of salps with random weight networks for feature selection

•An efficient wrapper feature selection approach is proposed.•The approach combines random weight networks and a time-varying swarm algorithm.•20 well-known datasets are utilized for comparison purposes.•Benchmark results confirm the effectiveness of the proposed model. Feature selection (FS) is con...

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Vydané v:Expert systems with applications Ročník 140; s. 112898
Hlavní autori: Faris, Hossam, Heidari, Ali Asghar, Al-Zoubi, Ala’ M., Mafarja, Majdi, Aljarah, Ibrahim, Eshtay, Mohammed, Mirjalili, Seyedali
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 01.02.2020
Elsevier BV
Predmet:
Algorithms
Evolutionary algorithms
Feature selection
Machine learning
Maximization
Optimization
Salp swarm algorithm
Weight
Salp swarm algorithm
Feature selection
Evolutionary algorithms
Optimization
ISSN:0957-4174, 1873-6793
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Shrnutí:•An efficient wrapper feature selection approach is proposed.•The approach combines random weight networks and a time-varying swarm algorithm.•20 well-known datasets are utilized for comparison purposes.•Benchmark results confirm the effectiveness of the proposed model. Feature selection (FS) is considered as one of the most common and challenging tasks in Machine Learning. FS can be considered as an optimization problem that requires an efficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.112898