C4.5: programs for machine learning
C4.5: programs for machine learning
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
An incremental genetic algorithm for classification and sensitivity analysis of its parameters
Expert Systems with Applications: An International Journal
Optimized fuzzy decision tree using genetic algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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Fuzzy rules are suitable for describing uncertain phenomena and natural for human understanding and they are, in general, efficient for classification. In addition, fuzzy rules allow us to effectively classify data having non-axis-parallel decision boundaries, which is difficult for the conventional attribute-based methods. In this paper, we propose an optimized fuzzy rule generation method for classification both in accuracy and comprehensibility (or rule complexity). We investigate the use of genetic algorithm to determine an optimal set of membership functions for quantitative data. In our method, for a given set of membership functions a fuzzy decision tree is constructed and its accuracy and rule complexity are evaluated, which are combined into the fitness function to be optimized. We have experimented our algorithm with several benchmark data sets. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with the existing methods including C4.5 and FID3.1 (Fuzzy ID3).