Inverse random under sampling for class imbalance problem and its application to multi-label classification

By Muhammad Atif Tahir, Josef Kittler, Fei Yan. Pattern Recognition, 2012

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fe16208bdcae1c83bb2b445d85ded5b897f87e37

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In Artificial Intelligence

Citations in the corpus

A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “…variables (e.g., Baccianella et al. [2009], S´anchez-Monedero et al. [2013], and P´erez-Ortiz et al. [2014]), multi-label classiﬁcation (e.g., Tahir et al. [2012] and Charte et al. [2015a, 2015b]), multi-instance learning (e.g., Wang et al. [2013a, 2013b]), and mining association rules…”
Dual autoencoders features for imbalance classification problem (2016, methodology): “The IRUS severely undersamples the majority class such that the number of samples in the majority class is smaller than that of the minority class in the training set for each base classifier.”
A novel ensemble method for classifying imbalanced data (2015, methodology): “One of the most popular methods for solving the class imbalance problems is sampling [20, 21, 22, 23].”
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification (2020, methodology): “Resampling methods either adopt over-sampling on tail classes [3, 14] or use under-sampling [8, 42, 21] on head classes.”
Addressing imbalance in multilabel classification: Measures and random resampling algorithms (2015, methodology): “To date, there are some proposals to deal with imbalanced MLDs focused in algorithmic adaptations of MLC algorithms [7] [8] [9], so they are classifier-dependent solutions.”
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation (2015, methodology): “…of Computer Science, University of Jaén, 23071 Jaén, Spain c Faculty of Computing and Information Technology – North Jeddah, King Abdulaziz University, 21589 Jeddah, Saudi Arabia a r t i c l e i n f o a b s t r a c t Article history: Received 14 April 2015 Received in revised form 16 June…”
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, background): “Data level solutions: The objective consists of rebalancing the class distribution by sampling the data space to diminish the effect of class imbalance, acting as an external approach [7,5,20,55,27,54].”