Rectangular Basis Functions Networks (RecBFN) come from RBF Networks, and are composed by a set of Fuzzy Points which describe the network. In this paper, a set of characteristics of the RecBF are proposed to be used in imbalanced datasets, especially the order of the training patterns. We will demonstrate that it is an important factor to improve the generalization of the solution, which is the main problem in imbalanced datasets. Finally, this solution is compared with other important methods to work with imbalanced datasets, showing our method works well with this type of datasets and that an understandable set of rules can be extracted. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Soler, V., & Prim, M. (2007). Rectangular basis functions applied to imbalanced datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4668 LNCS, pp. 511–519). Springer Verlag. https://doi.org/10.1007/978-3-540-74690-4_52
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