Enhancing rule interestingness for neuro-fuzzy systems

Data Mining Algorithms extract patterns from large amounts of data. But these patterns will yield knowledge only if they are interesting, i.e. valid, new, potentially useful, and understandable. Unfortunately, during pattern search most Data Mining Algorithms focus on validity only, which also holds true for Neuro-Fuzzy Systems. In this Paper we introduce a method to enhance the interestingness of a rule base as a whole. In the first step, we aggregate the rule base. through amalgamation of adjacent rules and eliminiation of redundant attributes. Supplementing this rather technical approach, we next sort rules with regard to their performance, as measured by their evidence. Finally, we compute reduced evidences, which penalize rules that are very similar to rules with a higher, evidence. Rules sorted on reduced evidence are fed into an integrated rulebrowser, to allow for manual rule selection according to personal and situation-dependent preference. This method was applied successfully to two real-life classification problems, the target group selection for a retail bank, and fault diagnosis for a large car manufacturer. Explicit reference is taken to the NEFCLASS algorithm, but the procedure is easily generalized to other systems.