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• W2604069132 abstract "In this paper, we introduced a new approach of combining multiple classifiers in a heterogeneous ensemble system. Instead of using numerical membership values when combining, we constructed interval membership values for each class prediction from the meta-data of observation by using the concept of information granule. In the proposed method, the uncertainty (diversity) of the predictions produced by the base classifiers is quantified by the interval-based information granules. The decision model is then generated by considering both bound and length of the intervals. Extensive experimentation using the UCI datasets has demonstrated the superior performance of our algorithm over other algorithms including six fixed combining methods, one trainable combining method, AdaBoost, bagging, and random subspace." @default.
• W2604069132 created "2017-04-07" @default.
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• W2604069132 date "2019-06-01" @default.
• W2604069132 modified "2023-10-16" @default.
• W2604069132 title "Aggregation of Classifiers: A Justifiable Information Granularity Approach" @default.
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• W2604069132 doi "https://doi.org/10.1109/tcyb.2018.2821679" @default.
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• W2604069132 hasPublicationYear "2019" @default.
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