Results 261 to 270 of about 222,810 (285)
Some of the next articles are maybe not open access.
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 Classifier Hub for Imbalanced Financial Data
2016We design and implement a classifier hub that can explore the detailed information on the imbalanced dataset and classify the dataset into two classes. Against the data imbalance, through setting imbalance ratio, it can adjust the proportion of majority and minority class.
Chirath Abeysinghe +2 more
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A review of methods for imbalanced multi-label classification
Pattern Recognition, 2021Adane 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