Erratum to xe2x80x9cEntropybased fuzzy support vector machine for imbalanced datasetsxe2x80x9d Knowl.Based Syst. 115 (2017) 87xe2x80x9399 Abstract   In this note, we show that the calculation of statistics      X    F    2      and      F    F      in sections 4.5 and 4.6 of the paper (Fan etxc2xa0al., 2017) is not correct. Also, based on the calculation of Critical Difference (CD) of DenferroniDunn test, we show that the conclusion on xe2x80x9dsignificantly outperforming the compared algorithmsxe2x80x9d, drawn by the authors using the    P   value test, is not convincing.

An UnderSampling Method with Support Vectors in Multiclass Imbalanced Data Classification Multiclass imbalanced data classification in supervised learning is one of the most challenging research issues in machine learning for data mining applications. Although several data sampling methods have been introduced by computational intelligence researchers in the past decades for handling imbalanced data, still learning from imbalanced data is a challenging task and played as a significant focused research interest as well. Traditional machine learning algorithms usually biased to the majority class instances whereas ignored the minority class instances. As a result, ignoring minority class instances may affect the prediction accuracy of classifiers. Generally, undersampling and oversampling methods are commonly used in single model classifiers or ensemble learning for dealing with imbalanced data. In this paper, we have introduced an undersampling method with support vectors for classifying imbalanced data. The proposed approach selects the most informative majority class instances based on the support vectors that help to engender decision boundary. We have tested the performance of the proposed method with single classifiers (C4.5 Decision Tree classifier and naive Bayes classifier) and ensemble classifiers (Random Forest and AdaBoost) on 13 benchmark imbalanced datasets. It is explicitly shown by the experimental result that the proposed method produces high accuracy when classifying both the minority and majority class instances compared to other existing methods.