{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T23:58:18Z","timestamp":1772063898849,"version":"3.50.1"},"reference-count":16,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T00:00:00Z","timestamp":1561593600000},"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 Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,10,25]]},"abstract":"<jats:p>Text classification technology, an important basis for text mining and information retrieval, is mainly to determine the text category according to the text content under a predetermined set of categories. Traditional manual text categorization has gradually failed to meet the needs, while automatic text categorization based on artificial intelligence has become an important research direction in the field of natural language processing. To this end, this paper introduced the RBNN-based classification algorithm by considering the high dimensionality, non-linearity and complex correlation between feature items, and the theoretical and feasibility analysis were carried out so as to apply it to text feature dimension reduction. Also, the effects of the distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the classification results were studied. Through the computer simulation experiment, the influence rule of distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the training precision and test accuracy of the classification process were demonstrated in the form of curves, which provides guidance for the application of RBNN in pattern recognition.<\/jats:p>","DOI":"10.3233\/jifs-179279","type":"journal-article","created":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T13:07:07Z","timestamp":1561727227000},"page":"4467-4475","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["RBNN application and simulation in big data set classification"],"prefix":"10.1177","volume":"37","author":[{"given":"Qin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Information Science, Xinxiang University, Xinxiang, China"}]},{"given":"Fred","family":"Wilson","sequence":"additional","affiliation":[{"name":"School of Mathematics and Information Science, Xinxiang University, Xinxiang, China"}]}],"member":"179","published-online":{"date-parts":[[2019,6,27]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2014.2356616"},{"issue":"128","key":"e_1_3_1_3_2","first-page":"1","article-title":"Blow-up criteria of smooth solutions to a 3D model of electro-kinetic fluids in a bounded domain","volume":"2016","author":"Miaochao Chen","year":"2016","unstructured":"MiaochaoChen and QilinLiu, Blow-up criteria of smooth solutions to a 3D model of electro-kinetic fluids in a bounded domain, Electronic Journal of Differential Equations 2016(128) (2016), 1\u20138.","journal-title":"Electronic Journal of Differential Equations"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2015.2511149"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2559-2"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2497286"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.02.029"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.02.034"},{"key":"e_1_3_1_9_2","first-page":"248","article-title":"Fuzzy C-means clustering based construction and training for second order RBF network","volume":"6493","author":"Tyagi K.","year":"2015","unstructured":"TyagiK., CaiX. and ManryM.T., Fuzzy C-means clustering based construction and training for second order RBF network, IEEE International Conference on Fuzzy Systems 6493 (2015), 248\u2013255.","journal-title":"IEEE International Conference on Fuzzy Systems"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10255-018-0740-3"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2017.2768366"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.05.198"},{"issue":"2","key":"e_1_3_1_13_2","first-page":"1","article-title":"Cost-sensitive radial basis function neural network classifier for software defect prediction","volume":"2016","author":"Kumudha P.","year":"2016","unstructured":"KumudhaP. and VenkatesanR., Cost-sensitive radial basis function neural network classifier for software defect prediction, The Scientific World Journal, 2016,(2016-9-21) 2016(2) (2016), 1\u201320.","journal-title":"The Scientific World Journal, 2016,(2016-9-21)"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22118"},{"issue":"4","key":"e_1_3_1_15_2","first-page":"50","article-title":"A selfadaptive fuzzy cmeans based radial basis function network to solve economic load dispatch problems","volume":"25","author":"Surekha P.","year":"2013","unstructured":"SurekhaP. and SumathiS., A selfadaptive fuzzy cmeans based radial basis function network to solve economic load dispatch problems, International Journal of Computer Applications 25(4) (2013), 50\u201359.","journal-title":"International Journal of Computer Applications"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2584107"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1166\/jmihi.2016.1901"}],"container-title":["Journal of Intelligent &amp; 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