Learning in datasets that suffer from imbalanced class distribution is an important problem in Pattern Recognition. This paper introduces a novel algorithm for data balancing, based on compact set clustering of the majority class. The proposed algorithm is able to deal with mixed, as well as incomplete data, and with arbitrarily dissimilarity functions. Numerical experiments over repository databases show the high quality performance of the method proposed in this paper according to area under the ROC curve and imbalance ratio. © Springer-Verlag 2013.
CITATION STYLE
Villuendas-Rey, Y., & García-Lorenzo, M. M. (2013). Mixed data balancing through compact sets based instance selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8258 LNCS, pp. 254–261). https://doi.org/10.1007/978-3-642-41822-8_32
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