{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T19:04:03Z","timestamp":1754161443272,"version":"3.41.2"},"reference-count":21,"publisher":"Emerald","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,8,24]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>The purpose of this paper is to find a novel optimization selection method for hyper-parameter of support vector classification (SVC), responsible for the classification of datasets from the UCI machine learning database repository.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Design\/methodology\/approach<\/jats:title>\n                  <jats:p>A novel two-stage optimization selection method for hyper-parameters is proposed. It makes use of explicit information derived from issues and implicit knowledge extracted from the evolution process so as to improve the performance of classifier. In the first stage, the search extent of each hyper-parameter is determined according to the requirements of issues. In the second stage, optimal hyper-parameters are obtained by adaptive chaotic culture algorithm in the above search extent. Adaptive chaotic cultural algorithm uses implicit knowledge extracted from the evolution process to control mutation scale of chaotic mutation operator. This algorithm can ensure the diversity of population and exploitation in the latter evolution.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>The rationality of the above optimization selection method is proved by the binary classification problem. Final confirmation of this approach is the classification results compared with other methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Originality\/value<\/jats:title>\n                  <jats:p>This optimization selection method can effectively avoid premature convergence and lead to better computation stability and precision. It is not related on the structure of functions. SVC model corresponding to optimal hyper-parameters by this method has better generalization.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1108\/17563781011066729","type":"journal-article","created":{"date-parts":[[2010,8,28]],"date-time":"2010-08-28T07:13:10Z","timestamp":1282979590000},"page":"449-462","source":"Crossref","is-referenced-by-count":3,"title":["The selection method for hyper-parameters of support vector classification by adaptive chaotic cultural algorithm"],"prefix":"10.1108","volume":"3","author":[{"given":"Yi-nan","family":"Guo","sequence":"first","affiliation":[{"name":"College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da-wei","family":"Xiao","sequence":"additional","affiliation":[{"name":"College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"2025072819005215800_b7","doi-asserted-by":"crossref","unstructured":"Burges, C.J.C.\n           (1998), \u201cA tutorial on support vector machines for pattern recognition\u201d, Data Mining and Knowledge Discovery, Vol. 2, pp. 121-67.","DOI":"10.1023\/A:1009715923555"},{"key":"2025072819005215800_b5","doi-asserted-by":"crossref","unstructured":"Chapelle, O.\n          , Vapnik, V., Bousquet, O. and Mukherjee, S. 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(2003), \u201cModel selection for support vector machine classification\u201d, Neurocomputing, Vol. 55, pp. 221-49.","DOI":"10.1016\/S0925-2312(03)00375-8"},{"key":"2025072819005215800_b11","doi-asserted-by":"crossref","unstructured":"Guo, X.C.\n           and Yang, J.H. (2008), \u201cA novel LS-SVMs hyper-parameter selection based on particle swarm optimization\u201d, Advances in Neural Information Processing, Vol. 71, pp. 3211-5.","DOI":"10.1016\/j.neucom.2008.04.027"},{"key":"2025072819005215800_b12","unstructured":"Guo, Y.-N.\n          , Wang, H. and Cheng, J. (2009), \u201cA novel adaptive chaotic cultural algorithm\u201d, Control and Decision, Vol. 4, pp. 514-9."},{"key":"2025072819005215800_b6","doi-asserted-by":"crossref","unstructured":"Huang, C.-L.\n           and Dun, J.-F. (2008), \u201cA distributed PSO-SVM hybrid system with feature selection and parameter optimization\u201d, Applied Soft Computing, Vol. 8, pp. 1381-91.","DOI":"10.1016\/j.asoc.2007.10.007"},{"key":"2025072819005215800_b14","doi-asserted-by":"crossref","unstructured":"Ismael, K.\n          , Salleh, S.H., Najeb, J.M. and Jahangir Bakhteri, R.B. (2008), \u201cEfficient parameter selection of support vector machines\u201d, 4th Kuala Lumpur International Conference on Biomedical Engineering, Vol. 21, Springer, Kuala Lumpur, pp. 183-6.","DOI":"10.1007\/978-3-540-69139-6_49"},{"key":"2025072819005215800_b15","doi-asserted-by":"crossref","unstructured":"Kerrthi, S.S.\n           (2002), \u201cEfficient tuning of SVM hyperparameters using radius margin bound and iterative algorithms\u201d, IEEE Transactions on Neural Networks, Vol. 5, pp. 1225-9.","DOI":"10.1109\/TNN.2002.1031955"},{"key":"2025072819005215800_b1","doi-asserted-by":"crossref","unstructured":"Lorena, A.C.\n           and de Carvalho, A.C.P.L.F. (2008), \u201cEvolutionary tuning of SVM parameter values in multiclass problems\u201d, Neurocomputing, Vol. 71, pp. 3326-34.","DOI":"10.1016\/j.neucom.2008.01.031"},{"key":"2025072819005215800_b17","doi-asserted-by":"crossref","unstructured":"Luo, Z.\n          , Wang, P., Li, Y., Zhang, W., Tang, W. and Xiang, M. 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