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Open AccessArticle

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

1
Centro de Investigación en Computación del Instituto Politécnico Nacional, Ciudad de Mexico 07700, Mexico
2
Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Ciudad de Mexico 07700, Mexico
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2779; https://doi.org/10.3390/app10082779
Received: 26 February 2020 / Revised: 2 April 2020 / Accepted: 9 April 2020 / Published: 16 April 2020
In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms. View Full-Text
Keywords: imbalanced datasets; associative classifiers; credit scoring imbalanced datasets; associative classifiers; credit scoring
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Rangel-Díaz-de-la-Vega, A.; Villuendas-Rey, Y.; Yáñez-Márquez, C.; Camacho-Nieto, O.; López-Yáñez, I. Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers. Appl. Sci. 2020, 10, 2779.

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