Rule Quality Measures Settings in Classification, Regression and Survival Rule Induction : an Empirical Approach

The paper presents the results of research related to the efficiency of the so-called rule quality measures which are used to evaluate the quality of rules at each stage of the rule induction. The stages of rule growing and pruning were considered along with the issue of conflict resolution which may occur during the classification. The work is the continuation of research on the efficiency of quality measures employed in sequential covering rule induction algorithm. In this paper we analyse only these quality measures (8 measures) which had been recognized as effective based on previous conducted research. The study was conducted on approximately 70 benchmark datasets related to classification, regression and survival analysis problems. In the comparisons we analyzed prognostic abilities of the induced rules as well as the complexity of the resulting rule-based data models.

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Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).