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A k-nearest neighbor (kNN) based bagging pruning algorithm for ensemble SVM classification is proposed in this paper. Redundant bags are discarded without reducing the performance of the ensemble SVM classifier. Ten VCI binary classification datasets are used to evaluate the performance of the proposed pruning algorithm against single SVM and bagging SVM classifiers. Results show that the proposed...
This paper compares empirically four bagging-based ensemble classifiers, namely the ensemble adaptive neuro-fuzzy inference system (ANFIS), the ensemble support vector machine (SVM), the ensemble extreme learning machine (ELM) and the random forest. The comparison of these four ensemble classifiers is novel because it has not been reported in the existing literature. The classifiers are evaluated...
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