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http://dx.doi.org/10.9708/jksci.2014.19.6.019

Context-Aware Fusion with Support Vector Machine  

Heo, Gyeong-Yong (Dept. of Electronic Engineering, Dong-Eui University)
Kim, Seong-Hoon (School of Computer Information, Kyungpook National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.19, no.6, 2014 , pp. 19-26 More about this Journal
Abstract
An ensemble classifier system is a widely-used multi-classifier system, which combines the results from each classifier and, as a result, achieves better classification result than any single classifier 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 the performance in current stage. Boosting is, however, a serial method which does not form a complete feedback loop. In this paper, proposed is context sensitive SVM ensemble (CASE) which adopts SVM, one of the best classifiers in term of classification rate, as a basic classifier and clustering 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 CASE achieves better result than boosting. Experimental results prove the usefulness of the proposed method.
Keywords
Ensemble; SVM; Context; Clustering;
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