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- W2590416935 abstract "This paper deals with the design of a weighted ensemble of classifiers to classify imbalance data having heterogeneous features. For this purpose, a meta ensemble model is created and instead of class labels, the output of each base classifier used in the ensemble model is transformed into a [class label, weight] pair to deal with the problem. The performances of the proposed method on various datasets are calculated using the measures - Classification accuracy and G-means. The results obtained from the proposed method are compared with other methods and the proposed one shows good results in most of the cases." @default.
- W2590416935 created "2017-03-03" @default.
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- W2590416935 date "2016-08-01" @default.
- W2590416935 modified "2023-09-25" @default.
- W2590416935 title "Design of a weighted meta classifier for imbalance data having heterogeneous features" @default.
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- W2590416935 doi "https://doi.org/10.1109/iccubea.2016.7860002" @default.
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