Diversity analysis on imbalanced data sets by using ensemble models

By Shuo Wang, Xin Yao. IEEE Symposium on Computational Intelligence and Data Mining, 2009

544 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c7c8134ea51302e1a956035008f3049a8fc883e3

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In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, background): “Since we have to select only two methodologies for the global analysis , we will stress SBAG as the best ranked method and RUSB , because it presents a robust behavior on average and the second best mean performance in two of the three algorithms .”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, methodology): “…assigning the corresponding rank to the method from which the UnderBagging [4] Bagging with undersampling of the majority class SBAB SMOTEBagging [51] Bagging where each bag ' s SMOTE quantity varies EASY EasyEnsemble [37] Bagging with undersampling of the majority class and AdaBoost difference…”
Cost-sensitive decision tree ensembles for effective imbalanced classification (2014, unknown): “We compared our method to SMOTEBagging, SMOTEBoost, IIvotes and EasyEnsemble.”
A novel ensemble method for classifying imbalanced data (2015, methodology): “Thus, some special sampling methods are combined with Bagging to solve this problem, including UnderBagging [39], OverBagging and SMOTEBagging [73].”
A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Namely, it was combined with boosting [Seiffert et al. 2010] and bagging [Chang et al. 2003; Tao et al. 2006; Wang and Yao 2009; Wallace et al. 2011] and was applied to both classes in random forests in a method named Balanced Random Forest (BRF) [Chen et al. 2004].”
Classification with class imbalance problem: A review (2015, methodology): “While diversity in ensemble methods has an extensive theoretical principle in regression problems, when it comes to classification, the concept of diversity is still largely undefined [102], [103], [100] and [104].”