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• W2762958719 abstract "This paper discusses the effect of locality and diversity among the base models of a Multi-Components Multi-Layer Predictive System (MCMLPS). A new ensemble method is introduced, where in the proposed architecture, the data instances are assigned to local regions using a conditional mutual information based on the similarity of their features. Furthermore, the outputs of the base models are weighted by this similarity metric. The proposed architecture has been tested on a number of data sets and its performance was compared to four benchmark algorithms. Moreover, the effect of changing three parameters of the proposed architecture has been tested and compared." @default.
• W2762958719 created "2017-10-20" @default.
• W2762958719 creator A5039995994 @default.
• W2762958719 creator A5083181106 @default.
• W2762958719 date "2017-01-01" @default.
• W2762958719 modified "2023-10-06" @default.
• W2762958719 title "Diversity and Locality in Multi-Component, Multi-Layer Predictive Systems: A Mutual Information Based Approach" @default.
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• W2762958719 doi "https://doi.org/10.1007/978-3-319-69179-4_22" @default.
• W2762958719 hasPublicationYear "2017" @default.
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