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Increasing Diversity in Random Forests Using Naive Bayes

Abstract : In this work a novel ensemble technique for generating random decision forests is presented. The proposed technique incorporates a Naive Bayes classification model to increase the diversity of the trees in the forest in order to improve the performance in terms of classification accuracy. Experimental results on several benchmark data sets show that the proposed method archives outstanding predictive performance compared to other state-of-the-art ensemble methods.
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Christos Aridas, Sotiris Kotsiantis, Michael Vrahatis. Increasing Diversity in Random Forests Using Naive Bayes. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.75-86, ⟨10.1007/978-3-319-44944-9_7⟩. ⟨hal-01557627⟩



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