Multi-matrices entropy discriminant ensemble learning for imbalanced problem
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DOI: 10.1007/s00521-019-04306-6
Abstract: The objective of this paper is to make an improvement on ensemble learning for imbalanced problem. Multi-matrices approach and nearest entropy are introduced into model of base classifier for the sake of utilizing spatial information… read more here.
Keywords: learning imbalanced; matrices entropy; imbalanced problem; multi matrices ... See more keywords
Multicriteria Classifier Ensemble Learning for Imbalanced Data
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DOI: 10.1109/access.2022.3149914
Abstract: One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion. Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss… read more here.
Keywords: multicriteria classifier; criterion; learning imbalanced; imbalanced data ... See more keywords
Learning From Imbalanced Data With Deep Density Hybrid Sampling
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2022.3151394
Abstract: Learning from imbalanced data is an important and challenging topic in machine learning. Many works have devised methods to cope with imbalanced data, but most methods only consider minority or majority classes without considering the… read more here.
Keywords: density; learning imbalanced; minority; imbalanced data ... See more keywords