Browse > Article
http://dx.doi.org/10.5391/JKIIS.2007.17.5.679

A Differential Evolution based Support Vector Clustering  

Jun, Sung-Hae (청주대학교 바이오정보통계학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.5, 2007 , pp. 679-683 More about this Journal
Abstract
Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.
Keywords
Support Vector Clustering;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. Mclachlan, D. Peel, Finite Mixture Models, John Wiley & Sons, 2000
2 UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/MLRepository.html
3 V. Vapnik, Statistical Learning Theory, John Wiley & Sons, 1998
4 최병인, 이정훈, 'Support Vector Machines를 이용한 Convex 클러스터 결합 알고리즘', 한국퍼지 및 지능시스템학회 2002 추계학술대회 논문지 pp 267-270, 2002
5 C. J. Burges, 'A Tutorial on Support Vector Machine for Pattern Recognition', Data Mining and Knowledge Discovery, Vol. 2, no. 2, pp. 121-167, 1998   DOI   ScienceOn
6 A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Springer, 2003
7 A. P. Engelbrecht, Computational Intelligence An Introduction, Wiley, 2002
8 J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann, 2001
9 S. Haykin, Neural Networks A Comprehensive Foundation, Prentice Hall, 1999
10 S. H. jun, Web Usage Mining Using Evolutionary Support Vector Machine, Lecture Note in Artificial Intelligence, Vol. 3809, pp. 1015-1020, 2005   DOI   ScienceOn
11 J. Lee, D. Lee, 'An Improved Cluster Labeling Method for Support Vector Clustering', IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, no. 3, pp. 461-464, 2005   DOI   ScienceOn
12 A. Ben-Hur, D. Hom, H. T. Siegelmann, V. Vapnik, 'Support Vector Clustering' , Journal of Machine Learning Research, Vol. 2, pp. 125-137, 2001   DOI
13 W. L. Martinez, A. R. Martinez, Computational Statistics Handbook with MATRAB, Chapman & Hall, 2002
14 최준혁, 전성해, 오경환, '통계적 학습이론을 이용한 최적군집화', 한국퍼지 및 지능 시스템학회 2005 추계 학술대회 논문지, 2005
15 J. C. Chiang, J. S. Wang, 'A Validity-Guided Support Vector Clustering Algorithm for Identification of Optimal Cluster Configuration', Proceeding of IEEE International Conference on Systems, Man and Cybernetics, pp. 3613-361, 2004
16 B. Y. Sun, D. S. Huang, 'Support Vector Clustering for Multiclass Classification Problems', Proceeding of IEEE Evolutionary Computation Congress, pp. 1480-1485, 2003
17 K. Krishna, K. Narasimha Murty, 'Genetic K-means algorithm', IEEE Transactions on Systems, Man and Cybernetics, part B, Vol. 29, no. 3, pp. 433-439, 1999   DOI   ScienceOn
18 S. M. Ross, Simulation, Academic Press, 1997
19 R. Storn, K. V. Price, 'Differential Evolution-a fast and efficient heuristic for global optimization over continuous spaces', Journal of Global Optimization, Vol. 11, pp. 341-359, 1997   DOI   ScienceOn