Context-Aware Fusion with Support Vector Machine (Support Vector Machine을 이용한 문맥 인지형 융합)

Gyeongyong Heo (허경용); Seong Hoon Kim (김성훈)

Context-Aware Fusion with Support Vector Machine

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2014, 19(6), pp.19-26
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Gyeongyong Heo ¹, Seong Hoon Kim ²

¹동의대학교
²경북대학교

Accredited

ABSTRACT

An ensemble classifier system is a widely-used multi-classifier system, which combines theresults from each classifier and, as a result, achieves better classification result than any singleclassifier used. Several methods have been used to build an ensemble classifier including boosting,which is a cascade method where misclassified examples in previous stage are used to boost theperformance in current stage. Boosting is, however, a serial method which does not form acomplete feedback loop. In this paper, proposed is context sensitive SVM ensemble (CASE) whichadopts SVM, one of the best classifiers in term of classification rate, as a basic classifier andclustering method to divide feature space into contexts. As CASE divides feature space and trains SVMs simultaneously, the result from one component can be applied to the other and CASEachieves better result than boosting. Experimental results prove the usefulness of the proposedmethod.

KEYWORDS

Ensemble, SVM, Context, Clustering

Citation status

* References for papers published after 2023 are currently being built.

[confproc] D. T. G. Dietterich / 2000 / Ensemble Methods in machine learning / Proceedings of the First International Workshop on Multiple Classifier Systems : 1~15

[journal] L. Rokach / 2010 / Ensemble-based classifiers / Artificial Intelligence Review 33(1-2) : 1~39

[journal] L. Breiman / 1996 / Bagging predictors / Machine Learning 24(2) : 123~140

[web] / Boosting. org / http://www.boosting.org

[journal] Robert E. Schapir / 1997 / A decision-theoretic generalization of on-line learning and an application to boosting / Journal of Computer and SystemSciences 55(1) : 119~139

[journal] Hyun-Chul Kim / 2003 / Constructing support vector machine ensemble / Pattern Recognition 36(12) : 2757~2767

[journal] Zhi-Yong Lin / 2009 / Several SVM Ensemble Methods Integrated with Under-Sampling for Imbalanced Data Learning / Advanced Data Mining and Applications, Lecture Notes in Computer Science 5678 : 536~544

[confproc] E. Ahmed / 2010 / Support Vector Machine ensembles using features distribution among subsets for enhancing microarray data classification / Proceedings of the 10th International Conference on Intelligent Systems Design and Applications : 1242~1246

[book] J. C. Bezdek / 1981 / Pattern Recognition with Fuzzy Objective Function Algorithms / Plenum Press

[book] V. Vapnik / 1998 / Statistical Learning Theory / John Wiley & Sons

[journal] K. Wu / 2006 / Fuzzy SVM for content-based image retrieval / IEEE Computational Intelligence Magazine 1(2) : 10~16

[confproc] B. Lim / 2006 / Web search with text categorization using probabilistic framework of SVM / Proceedings of the IEEE International Conference on Systems, Man and Cybernetics : 2950~2955

[journal] C. F. Lin / 2002 / Fuzzy support vector machines / IEEE Transactions on Neural Networks 13(2) : 464~471

[journal] Y. Wang / 2005 / A new fuzzy support vector machine to evaluate credit risk / IEEE Transactions on Fuzzy Systems 13(6) : 820~831

[confproc] L. Wang / 2004 / Incorporating prior knowledge into SVM for image retrieval / Proceedings of the 17th International Conference on Pattern Recognition : 981~984

[journal] Q. Tao / 2005 / Posterior probability support vector machines for unbalanced data / IEEE Transactions on Neural Networks 16(6) : 1561~1573

[journal] Y. Liu / 2007 / Soft SVM and its application in video-object extraction / IEEE Transactions on Signal Processing 55(7) : 3272~3282

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65