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An improved fuzzy classifier for imbalanced data
Journal of Intelligent & Fuzzy Systems, 2017Selecting model between recognition rate of “large” class and recognition rate of “small” class in imbalanced data is often a serious trade-off. Most approaches emphasize the accuracy of “large” class. The drawback is that potentially informative “small” class may be overlooked and even make an overfitting model.
Dandan Yan, Youlong Yang, Benchong Li
openaire +1 more source
2011
An imbalanced training dataset can pose serious problems for many real-world data-mining tasks that conduct supervised learning. In this chapter,\(^\dagger\) we present a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy.
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An imbalanced training dataset can pose serious problems for many real-world data-mining tasks that conduct supervised learning. In this chapter,\(^\dagger\) we present a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy.
openaire +1 more source
A review of methods for imbalanced multi-label classification
Pattern Recognition, 2021Adane Nega Tarekegn +2 more
exaly
Evidential Combination of Classifiers for Imbalanced Data
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022Jiawei Niu +3 more
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A Survey of Predictive Modeling on Imbalanced Domains
ACM Computing Surveys, 2017Paula Branco +2 more
exaly