An evidence theoretic ensemble design technique

Abstract

Ensemble design techniques based on resampling the training set are successfully used to improve the classification accuracies of the base classifiers. In Boosting technique, each training set is obtained by drawing samples with replacement from the available training set according to a weighted distribution which is iteratively updated for generating new classifiers for the ensemble. The resultant classifiers are accurate in different parts of the input space mainly specified the sample weights. In this study, a dynamic integration of boosting based ensembles is proposed so as to take into account the heterogeneity of the input sets. In this approach, a Dempster-Shafer theory based framework is developed to consider the training sample distribution in the restricted input space of each test sample. The effectiveness of the proposed technique is compared to AdaBoost algorithm using nearest mean type base classifier.