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Its efficiency and accuracy mainly depend on the exceptional feature subset and optimal parameters. In this paper, a new feature selection method and an improved particle swarm optimization algorithm are proposed to improve the efficiency and the classification accuracy of the SVM. The new feature selection method, named Feature Selection-score (FS-score), performs well on data sets. If a feature makes the class external sparse and the class internal compact, its FS-score value will be larger and the probability of being selected will be greater. An improved particle swarm optimization model with dynamic adjustment of inertia weight (DWPSO-SVM) is also proposed to optimize the parameters of the SVM. By improving the calculation method of the inertia weight of the particle swarm optimization (PSO), inertia weight can decrease nonlinearly with the number of iterations increasing. In particular, the introduction of random function brings the inertia weight diversity in the later stage of the algorithm and the global searching ability of the algorithm to avoid falling into local extremum. The experiment is performed on the standard UCI data sets whose features are selected by the FS-score method. Experiments demonstrate that our algorithm achieves better classification performance compared with other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/e25030531","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T04:05:23Z","timestamp":1679285123000},"page":"531","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["A Hybrid Particle Swarm Optimization Algorithm with Dynamic Adjustment of Inertia Weight Based on a New Feature Selection Method to Optimize SVM Parameters"],"prefix":"10.3390","volume":"25","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Media Science & School of Journalism, Northeast Normal University, Jingyue, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Media Science & School of Journalism, Northeast Normal University, Jingyue, Changchun 130117, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongfei","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Qianjin, Changchun 130012, China"},{"name":"College of Computer Science and Technology, Jilin University, Qianjin, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacong","family":"Yi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Qianjin, Changchun 130012, China"},{"name":"College of Computer Science and Technology, Jilin University, Qianjin, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118931","DOI":"10.1109\/ACCESS.2019.2933437","article-title":"The OCS-SVM: An objective-cost-sensitive SVM with sample-based misclassification cost invariance","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2355","DOI":"10.1007\/s00521-016-2818-2","article-title":"A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture","volume":"30","author":"Faris","year":"2018","journal-title":"Neural Comput. 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