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To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc.<\/jats:p>","DOI":"10.3233\/ida-216493","type":"journal-article","created":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T15:37:00Z","timestamp":1680881820000},"page":"399-415","source":"Crossref","is-referenced-by-count":9,"title":["A Relief-PGS algorithm for feature selection and data classification"],"prefix":"10.1177","volume":"27","author":[{"given":"Youming","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Control and Intelligent Process (ACIP), Xi\u2019an, Shaanxi, China"}]},{"given":"Jiali","family":"Han","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]},{"given":"Tianqi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]}],"member":"179","reference":[{"issue":"9","key":"10.3233\/IDA-216493_ref1","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.datak.2018.07.004","article-title":"Uncertain data classification with additive kernel support vector machine","volume":"117","author":"Xie","year":"2018","journal-title":"Data & Knowledge Engineering"},{"issue":"1","key":"10.3233\/IDA-216493_ref2","first-page":"586","article-title":"SVM based multi-label learning with missing labels for image annotation","volume":"126","author":"Liu","year":"2018","journal-title":"Pattern Recognition"},{"issue":"2","key":"10.3233\/IDA-216493_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2013.11.009","article-title":"A unified SVM framework for signal estimation","volume":"26","author":"Rojo-\u00c1lvarez","year":"2014","journal-title":"Digital Signal Processing"},{"issue":"5","key":"10.3233\/IDA-216493_ref4","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/j.cogsys.2018.09.022","article-title":"Novel object detection and recognition system based on points of interest selection and SVM classification","volume":"52","author":"Bhuvaneswari","year":"2018","journal-title":"Cognitive Systems Research"},{"issue":"9","key":"10.3233\/IDA-216493_ref5","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1016\/j.apr.2019.04.005","article-title":"Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm","volume":"10","author":"Liu","year":"2019","journal-title":"Atmospheric Pollution Research"},{"key":"10.3233\/IDA-216493_ref6","doi-asserted-by":"crossref","unstructured":"L. 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