10.25905/5df05c6a78196
Hossam Faris
Hossam
Faris
Ala Al-Zoubi
Ala
Al-Zoubi
Majdi Mafarja
Majdi
Mafarja
Ibrahim Aljarah
Ibrahim
Aljarah
Mohammed Eshtay
Mohammed
Eshtay
Seyedali Mirjalili
Seyedali
Mirjalili
Time-Varying Hierarchical Chains of Salps with Random Weight Networks for Feature Selection
Torrens University Australia
2019
Feature Selection
Salp Swarm
Algorithm
Optimization
Artificial Intelligence and Image Processing
2019-12-11 03:03:04
Journal contribution
https://torrens.figshare.com/articles/journal_contribution/Time-Varying_Hierarchical_Chains_of_Salps_with_Random_Weight_Networks_for_Feature_Selection/11351159
Feature selection (FS) is considered asone of the most common and challenging tasks in MachineLearning. FScanbeconsideredasanoptimizationproblemthatrequiresanefficient 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.<div><br></div><div>Faris, H., Heidari, A. A., Al-Zoubi, A. M., Mafarja, M., Aljarah, I., Eshtay, M., & Mirjalili, S. (2020). Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Systems with Applications, 140. https://doi.org/10.1016/j.eswa.2019.112898<br></div>