Considerations on the principle of rule induction by STRIM and its relationship to the conventional Rough Sets methods.

Abstract STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induce if–then rules from the decision table, and its effectiveness has been confirmed by simulation experiments. The method was studied independently of the conventional rough sets methods. This paper summarizes the basic notion of the conventional rule induction methods and newly formulates the idea of STRIM, and then considers the relationship between STRIM and conventional methods, especially VPRS (Variable Precision Rough Set), and shows that STRIM develops the notion of VPRS into a statistical principle. In a simulation experiment, we also consider the condition that STRIM induces the true rules specified in advance. On the other hand, real-world datasets are often small and/or contain missing and contaminated values in the decision table from various reasons. In order to apply STRIM to real-world datasets, we examine the capacity of STRIM in such circumstances by a simulation experiment, after studying the question of what size dataset is required for STRIM. Such studies and examinations are very important to confirm if STRIM is properly applied to real-world datasets, and the results are reasonable. Graphical abstract Highlights • The rule induction by STRIM and the conventional Rough Sets, particularly, VPRS. • The relationships and differences between them. • The superiority of STRIM to the conventional methods at various points. [ABSTRACT FROM AUTHOR]