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http://dx.doi.org/10.9708/jksci.2013.18.7.037

Context Dependent Fusion with Support Vector Machines  

Heo, Gyeongyong (Dept. of Electronic Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Society of Computer and Information / v.18, no.7, 2013 , pp. 37-45 More about this Journal
Abstract
Context dependent fusion (CDF) is a fusion algorithm that combines multiple outputs from different classifiers to achieve better performance. CDF tries to divide the problem context into several homogeneous sub-contexts and to fuse data locally with respect to each sub-context. CDF showed better performance than existing methods, however, it is sensitive to noise due to the large number of parameters optimized and the innate linearity limits the application of CDF. In this paper, a variant of CDF using support vector machines (SVMs) for fusion and kernel principal component analysis (K-PCA) for context extraction is proposed to solve the problems in CDF, named CDF-SVM. Kernel PCA can shape irregular clusters including elliptical ones through the non-linear kernel transformation and SVM can draw a non-linear decision boundary. Regularization terms is also included in the objective function of CDF-SVM to mitigate the noise sensitivity in CDF. CDF-SVM showed better performance than CDF and its variants, which is demonstrated through the experiments with a landmine data set.
Keywords
Fusion; Fuzzy; Context; Support vector machine(SVM);
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