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This paper proposes a new strategy - support-based SVM ensemble for building credit scoring systems. Different from the commonly used "one-member-one-vote" majority-ruled ensembles, our proposed new framework aggregates degrees of support, or confidence levels, of several SVM classifiers to generate the final classification results that represent the consensus of the SVM. Decision values...
This paper presents a method of combining support vector machine (SVM) based on fuzzy integral. The classification has two steps: first map individual SVM classifiers' decision values, which are good representatives of confidence, to memberships, second aggregate these memberships by fuzzy integral to obtain the final decision. Experimental results on two public datasets indicate that the performance...
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