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Investigating Accuracies of Classifications for Randomized Imbalanced Class Distributions

Abe H., Tsumoto S.

Fundamenta Informaticae

2009

Vol. 90, nr 4

369-378

In datamining post-processing, rule selection with objective rule evaluation indices is one of useful methods for extracting valuable knowledge from mined patterns. However, the relationship between an index value and experts’ criteria has never been clarified. In order to determine the relationship, we have developed a method to obtain learning models from a dataset consisting of objective rule evaluation indices and evaluation labels for rules. In this study, we have compared accuracies of classification learning algorithms for datasets with randomized class labels. Then, the result shows that accuracies of classification learning algorithms without any criterion of a human expert can not outperform each percentage of majority class on both of the balanced and imbalanced class distribution datasets. With regarding to this result, we can determine whether or not a labeled rule set contains some criteria based on the dataset consisting the objective rule evaluation indices.