Journal Article10.1109/TKDE.2012.232
MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning
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TL;DR: A new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems and is better than or comparable with some other existing methods in terms of various assessment metrics.
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Abstract: Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).
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Citations
Oversampling the Minority Class in the Feature Space
TL;DR: The general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space) is explored, where the classes will be linearly separable and synthetically generated patterns will lie on the minority class region.
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Improving interpolation-based oversampling for imbalanced data learning
TL;DR: This study identifies that the flaws of the over constraint, low-efficiency expansion, and over generalization can occur when interpolating the synthetic samples for the inland minority, borderline minority, and trapped minority samples and proposes a Position characteristic-Aware Interpolation Oversampling algorithm.
77
NI-MWMOTE: An improving noise-immunity majority weighted minority oversampling technique for imbalanced classification problems
TL;DR: This work proposes an improving noise-immunity majority weighted minority oversampling technique abbreviated NI-MWMOTE, which not only avoids the generation of new noise, but also effectively overcomes both between-class imbalances and within-classImbalances.
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Class-Imbalanced Deep Learning via a Class-Balanced Ensemble
TL;DR: In this paper, an ensemble of auxiliary classifiers branching out from various hidden layers of a CNN is trained together with the CNN in an end-to-end manner to rectify the bias toward the majority classes.
75
Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems
TL;DR: Three proposed sampling approaches for imbalanced learning are presented, based on a new class imbalance metric, termed entropy-based imbalance degree (EID), considering the differences of information contents between classes instead of traditional imbalance-ratio.
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References
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.