Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

By Salvador Garcia, Francisco Herrera. Evolutionary Computation, 2009

426 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/66bdd1725684a173f9b2251e923db173221b5354

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In Artificial Intelligence

Citations in the corpus

A survey on addressing high-class imbalance in big data (2018, methodology): “EUS provides a fitness function for a prototype selection method, where the fitness function aims to find the proper balance between reduction (under-sampling) of training data instances and classification performance [52].”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “Finally, the application of genetic algorithms or particle swarm optimization for the correct identification of the most useful instances has shown to achieve good results [53,142].”
On the effectiveness of preprocessing methods when dealing with different levels of class imbalance (2012, methodology): “Hongyu and Herna [33] introduced the DataBoost-IM method, which combines boosting and data generation.”
Classification with class imbalance problem: A review (2015, methodology): “[60] Evolutionary undersampling Straight forward approach and widely used in many domain applications Risk of over fitting”
On the use of MapReduce for imbalanced big data using Random Forest (2014, methodology): “Oversampling methods [11,12,26] aim to balance the class distribution adding to the new dataset instances from the minority class; undersampling methods [22,30,52] try to adjust the class proportion deleting instances from the majority class; and hybrid methods [6] combine the two previous approache”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, neutral): “For example, Luengo et al. (2011) analyze the behavior of different sampling methods, including SMOTE, one of its extensions called SMOTE-ENN, and an under-sam-pling method named EUSCHC (García & Herrera, 2009).”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, methodology): “(1)) could also be used as a performance measure, but in our case, following the empirical analysis carried out in [23], we use the GM since it has less over-ﬁ tting problems and performs better in EUS (even though AUC is then used as evaluation criterion).”
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].”