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• W2554711080 abstract "Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which applies local classification results of classifiers among nearest neighbors to build a local classifier ensemble. From this dynamically selected process, discriminative classifiers are weighted heavily to build a locally discriminative ensemble. Experimental results on several UCI datasets have shown that, our proposed method achieves best classification performance among individual classifiers, majority voting and AdaBoost algorithms." @default.
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• W2554711080 date "2016-01-01" @default.
• W2554711080 modified "2023-09-28" @default.
• W2554711080 title "Building Locally Discriminative Classifier Ensemble Through Classifier Fusion Among Nearest Neighbors" @default.
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• W2554711080 doi "https://doi.org/10.1007/978-3-319-48890-5_21" @default.
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