Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Abstract

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.

Evaluation of Supervised machine learning classifiers for detecting the degrees of possibility of Coronavirus disease infection

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Special Issue on Data Preprocessing in Pattern Recognition: Recent Progress, Trends and Applications

“…A total of ten high-quality and peer-reviewed papers form this Special Issue, covering the following topics: class imbalance [1][2][3][4][5][6], big data preprocessing [1], prototype selection [7,8], variable selection [9] and clustering data on arbitrary shape [10].…”

“…Duan et al [2] propose a two-step solution for two-class problems using a novel classifier ensemble framework based on K-means and the oversampling technique called ADASYIN. Rangel-Díaz-dela-Vega et al [3] performed an experimental study on the behavior of four associative classifiers trained on resampled imbalanced credit scoring datasets. Gul et al [4] deal with the class imbalance problem for a theft electricity detection problem using a five-step framework incorporating several data preprocessing techniques.…”