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- W2913506004 abstract "Ensemble learning, which aggregates multiple base (weak) learners to obtain a strong learner, is an effective approach for improving the generalization performance of a machine learning model. Several completely unsupervised ensemble learning approaches have been proposed in the literature for binary classification. However, most of them only considered the case that the two classes are balanced, and hence their performances deteriorate when there is significant class imbalance, which often happens in practice. This paper proposes a spectral meta-learner for class imbalance (SMLCI) approach to explicitly consider the class imbalance. Experiments on 12 UCI datasets from various domains verified that SMLCI significantly outperformed the individual base classifiers, and also five existing unsupervised ensemble learning approaches, when the balanced classification accuracy is used as the performance measure." @default.
- W2913506004 created "2019-02-21" @default.
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- W2913506004 date "2018-11-01" @default.
- W2913506004 modified "2023-09-26" @default.
- W2913506004 title "Unsupervised Ensemble Learning for Class Imbalance Problems" @default.
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- W2913506004 doi "https://doi.org/10.1109/cac.2018.8623590" @default.
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