FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning

By Rukshan Batuwita, Vasile Palade. IEEE transactions on fuzzy systems, 2010

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A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Applying a method that changes the data distribution to obtain a more balanced one is an effective solution to the imbalance problem [Estabrooks et al. 2004; Batuwita and Palade 2010a; Fern´andez et al. 2008, 2010].”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “For example, Batuwita and Palade developed the FSVM-CIL algorithm [13], a synergy between SVMs and fuzzy logic aimed to reflect the within-class importance of different training examples in order to suppress the effect of outliers and noise.”
Robust support vector machines based on the rescaled hinge loss function (2017, background): “In Algorithm 1, v0 can be initialized by either setting it to 1 − or using some existing weight functions [9,10].”
Training and assessing classification rules with imbalanced data (2012, methodology): “This is the route followed, for instance, by Riddle et al. (1994) and Cieslak and Chawla (2008) when using decision trees, Veropoulos et al. (1999) and Batuwita and Palade (2010) when support vector machines are used and Barandela et al. (2003) that consider k − nearest neighbor classiﬁers.”