Strimの実データへの適用と拡張strimの提案

Abstract

We have previously proposed a statistical test rule induction method (STRIM), which induces the causality by if-then rules hiding in the dataset called the decision table in the field of the Rough Sets and confirmed its validity in a simulation model. However, the task of studying its validity and usefulness in a real-world dataset (RWD) was left to future research. Generally, the result of rule induction from an RWD cannot be directly ascertained. Therefore, after the previous STRIM was applied to an RWD, the induced rules were applied to the classification problem, and the result of classification was recognized as the validity of the rule induction method because the result was directly affected by the induced rules. Here, the classification result by Random Forest (RF) was used for an index of validity of that by the previous STRIM.Specifically, the following were experimentally and statistically studied: (1) The result by the previous STRIM was inferior to that by RF in an RWD, although the former was superior to the latter in the simulation dataset. (2) The previous was improved into ex-STRIM, and the improved showed superior results to that by RF in the simulation and equal to that by RF in the RWD. (3) Trunk rules by tr-STRIM were extracted from the rules by the previous STRIM, and the inclusion relationships between the previous, ex-STRIM, and tr-STRIM were considered to discuss various levels of rule description in the RWD.