{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:51:43Z","timestamp":1776811903918,"version":"3.51.2"},"reference-count":15,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"2","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p>Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy.<\/jats:p>","DOI":"10.3233\/jcm-247335","type":"journal-article","created":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T11:45:54Z","timestamp":1715341554000},"page":"1253-1266","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimization of parallel SVM algorithm for big data"],"prefix":"10.66113","volume":"24","author":[{"given":"Rui","family":"Xue","sequence":"first","affiliation":[{"name":"Henan Finance University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Cai","sequence":"additional","affiliation":[{"name":"Henan Finance University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","published-online":{"date-parts":[[2024,5]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1374-6"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108219"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-021-02787-9"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.06.010"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.2112\/SI93-110.1"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/minf.202000009"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1177\/00405175211059207"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.01.031"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2019.0289"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101813"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.03.011"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.2112\/SI94-106.1"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2021.07.022"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0414-6"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.04.025"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCM-247335","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JCM-247335","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCM-247335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:28Z","timestamp":1776809248000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JCM-247335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":15,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.3233\/JCM-247335"],"URL":"https:\/\/doi.org\/10.3233\/jcm-247335","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}