Improved salp swarm algorithm based on particle swarm optimization for feature selection

• Published in: Journal of ambient intelligence and humanized computing Vol. 10; no. 8; pp. 3155 - 3169
• Main Authors: Ibrahim, Rehab Ali, Ewees, Ahmed A., Oliva, Diego, Abd Elaziz, Mohamed, Lu, Songfeng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Classification; Computational Intelligence; Datasets; Engineering; Feature selection; Foraging behavior; Gene expression; Genetic algorithms; Heuristic; Image retrieval; Machine learning; Methods; Optimization techniques; Original Research; Particle swarm optimization; Performance evaluation; Robotics and Automation; User Interfaces and Human Computer Interaction; Salp swarm algorithm; Swarm techniques; Feature selection; Global optimization; Particle swarm optimization
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-018-1031-9

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.