Interactive Fuzzy Data Mining by Evolutionary Multiobjective Optimization with User Preference

Abstract

In this study, we propose an interactive fuzzy data mining technique from time-series data. In our method, fuzzy rule-based systems are extracted as knowledge by evolutionary multiobjective optimization with user preference. A user often does not know own preference before obtaining some solutions. The preference is sometime changeable during search process. To deal with these situations, we define an objective function changeable according to user preference anytime. We simultaneously optimize two objectives: accuracy maximization and user preference maximization.