Globally Optimal Fuzzy Decision Trees for Classification and Regression
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IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
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IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
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PaintingClass: interactive construction, visualization and exploration of decision trees
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Information Sciences—Informatics and Computer Science: An International Journal
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
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Neuro-fuzzy classification of prostate cancer using NEFCLASS-J
Computers in Biology and Medicine
An experimental evaluation of ensemble methods for EEG signal classification
Pattern Recognition Letters
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Applied Soft Computing
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Computational Statistics & Data Analysis
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Fuzzy Sets and Systems
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ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams
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ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
An Optimized Parallel Decision Tree Model Based on Rough Set Theory
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Interpretability constraints for fuzzy information granulation
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Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
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Expert Systems with Applications: An International Journal
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Information Sciences: an International Journal
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ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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Intelligent Data Analysis
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International Journal of Artificial Intelligence and Soft Computing
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FAW'07 Proceedings of the 1st annual international conference on Frontiers in algorithmics
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IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. We present another modification, aimed at combining symbolic decision trees with approximate reasoning offered by fuzzy representation. The intent is to exploit complementary advantages of both: popularity in applications to learning from examples, high knowledge comprehensibility of decision trees, and the ability to deal with inexact and uncertain information of fuzzy representation. The merger utilizes existing methodologies in both areas to full advantage, but is by no means trivial. In particular, knowledge inferences must be newly defined for the fuzzy tree. We propose a number of alternatives, based on rule-based systems and fuzzy control. We also explore capabilities that the new framework provides. The resulting learning method is most suitable for stationary problems, with both numerical and symbolic features, when the goal is both high knowledge comprehensibility and gradually changing output. We describe the methodology and provide simple illustrations