Correlation Assessment of the Performance of Associative Classifiers on Credit Datasets Based on Data Complexity Measures

One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of the associative classifiers derived from Alpha-Beta models applied to the financial field. In this paper, the behavior of four associative classifiers was studied: the One-Hot version of the Hybrid Associative Classifier with Translation (CHAT-OHM), the Extended Gamma (EG), the Naïve Associative Classifier (NAC), and the Assisted Classification for Imbalanced Datasets (ACID). To establish the performance, we used the area under the curve (AUC), F-score, and geometric mean measures. The four classifiers were applied over 11 datasets from the financial area. Then, the performance of each one was analyzed, considering their correlation with the measures of data complexity, corresponding to six categories based on specific aspects of the datasets: feature, linearity, neighborhood, network, dimensionality, and class imbalance. The correlations that arise between the measures of complexity of the datasets and the measures of performance of the associative classifiers are established; these results are expressed with Spearman’s Rho coefficient. The experimental results correctly indicated correlations between data complexity measures and the performance of the associative classifiers.