The ensemble methods are meta-algorithms that combine several base machine learning techniques to produce one optimal predictive model. Many existing committees of classifiers use feature space partitioning to determine the decision regions in which the selection of base classifiers occur or to learn a diverse ensemble of base classifiers. Furthermore, the division of the feature space into subspaces does not depend on the predictive model that defines the decision boundaries. Therefore, we propose a novel ensemble learning algorithm based on the feature space partitioning defined by decision tree boundaries. In addition, in a proposed framework, the feature subspace selection occurs taking class label imbalance ratio into account. The proposed method defines the ensemble class label based on selected previously feature subspaces and a neighborhood of feature subspaces defined by its reference point. Experimental results indicate that our proposed method is more effective than state-of-the-art ensemble methods on twenty-seven benchmark datasets regarding seven representative classification performance measures. (C) 2022 Elsevier Inc. All rights reserved.
Combining classifiersEnsemble of classifiersDecision treeMachine learningDecision boundaryENSEMBLECLASSIFIERSSELECTIONSYSTEMMODEL
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Elsevier
