Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5516
Title: Feature selection using salp swarm algorithm with chaos
Authors: Ahmad, Subhi
Mafarja, Majdi
Faris, Hossam
Aljarah, Ibrahim
Keywords: Data structures (Computer science);Mathematical optimization;Artificial intelligence;Heuristic algorithms;Algorithms;Machine learning;Database management;Computer network architectures
Issue Date: 24-Mar-2018
Publisher: ACM
Abstract: The performance of classification algorithms is highly sensitive to the data dimensionality. High dimensionality may cause many problems to a classifier like overfitting and high computational time. Feature selection (FS) is a key solution to both problems. It aims to reduce the number of features by removing the irrelevant, redundant and noisy data, while trying to keep an acceptable classification accuracy. FS can be formulated as an optimization problem. Metaheuristic algorithms have shown superior performance in solving this type of problems. In this work, a chaotic version of Salp Swarm Algorithm (SSA) is proposed, which is considered one of the recent metaheuristic algorithms. The proposed approach is applied for the first time on feature selection problems. Four different chaotic maps are used to control the balance between the exploration and exploitation in the proposed approach. The proposed approaches are evaluated using twelve real datasets. The comparative results shows that the chaotic maps significantly enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature.
Description: The paper was presented in the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI 2018) which was held in Phuket, Thailand during March 24-25, 2018
URI: http://hdl.handle.net/20.500.11889/5516
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