Abstract
In this paper, an improved support vector machine using a stochastic local search (SVM+SLS) is studied for the classification problem in Datamining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experiments on some datasets are performed to show and compare the effectiveness of the proposed approach. The computational experiments show that the proposed SVM+SLS provides competitive results and finds high quality solutions.
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References
Phyu, T.N.: Survey of Classification Techniques in Data Mining. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, pp. 18–20 (2009)
Burgers, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)
Tan, K.C., Teoh, E.J., Yua, Q., Goh, K.C.: A Hybrid Evolutionary Algorithm for Attribute Selection in Data Mining. Expert Syst. with Appl. 36, 8616–8630 (2009)
Boughaci, D., Benhamou, B., Drias, H.: Local Search Methods for the Optimal Winner Determination Problem in Combinatorial Auctions. J. Math. Modell. Algorithm 9, 165–180 (2010)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Massachusetts (2006)
Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, New York (1998)
Vapnik, V.N.: The Natural of Statistical Learning theory. Springer, New York (1995)
Kecman, V.: Learning and Soft Computing: Support Vector machines. Neural Networks, and Fuzzy logic Models. The MIT Press, London (2001)
Li, Y., Tong, Y., Bai, B., Zhang, Y.: An Improved Particle Swarm Optimization for SVM Training. In: Third International Conference on Natural Computation (ICNC 2007), pp. 611–615 (2007)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Mateo (2006)
Zhili, W.: Kernel Based Learning Methods for Pattern and Feature Analysis. Ph.D Thesis Hong Kong Baptist University (2004)
Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines (2001), Data sets available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/data/
Waikato Environment for Knowledge Analysis (WEKA), Version 3.6.6 (c) 1999-2011, The University of Waikato, Hmilton, New Zealand, Software available at http://www.cs.waikato.ac.nz/~ml/weka/
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Nekkaa, M., Boughaci, D. (2012). Improving Support Vector Machine Using a Stochastic Local Search for Classification in DataMining. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_21
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DOI: https://doi.org/10.1007/978-3-642-34481-7_21
Publisher Name: Springer, Berlin, Heidelberg
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