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>