Artificial Life and Robotics

, 14:418 | Cite as

Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems

Original Article

Abstract

In the design of fuzzy-rule-based systems, we have two conflicting objectives: accuracy maximization and interpretability maximization. As a measure of interpretability, a number of criteria have been proposed in the literature. Most of those criteria have been incorporated into fitness functions in order to automatically find accurate and interpretable fuzzy systems by genetic algorithms. However, interpretability is very subjective and is rarely defined for any users beforehand. In this article, we propose the incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems. User preference is represented by a preference function which is changeable according to the user’s direct manipulation during evolution. The preference function is used as one of the objective functions in multi-objective genetic fuzzy rule selection. The effectiveness of the proposed method is examined through some case studies for the design of fuzzyrule-based classifiers.

Key words

Multi-objective genetic fuzzy systems Fuzzyrule-based systems User preference Interactive genetic algorithms Pattern classification problems 

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Copyright information

© International Symposium on Artificial Life and Robotics (ISAROB). 2009

Authors and Affiliations

  1. 1.Department of Computer Science and Intelligent Systems, Graduate School of EngineeringOsaka Prefecture UniversitySakai, OsakaJapan

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