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Chawla, N.V., Lazarevic, A., Hall, L.O. and Bowyer, K.W. (2003) SMOTEBoost: Improving Prediction of the Minority Class in Boosting. Proceeding of 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, 22-26 September 2003, 107-119.
http://dx.doi.org/10.1007/978-3-540-39804-2_12

has been cited by the following article:

• TITLE: A KNN Undersampling Approach for Data Balancing

  AUTHORS: Marcelo Beckmann, Nelson F. F. Ebecken, Beatriz S. L. Pires de Lima

  KEYWORDS: Machine Learning, Class Overlaping, Imbalanced Datases

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.7 No.4, November 11, 2015

  ABSTRACT: In supervised learning, the imbalanced number of instances among the classes in a dataset can make the algorithms to classify one instance from the minority class as one from the majority class. With the aim to solve this problem, the KNN algorithm provides a basis to other balancing methods. These balancing methods are revisited in this work, and a new and simple approach of KNN undersampling is proposed. The experiments demonstrated that the KNN undersampling method outperformed other sampling methods. The proposed method also outperformed the results of other studies, and indicates that the simplicity of KNN can be used as a base for efficient algorithms in machine learning and knowledge discovery.