| Resumen |
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. |
