Compact Rule Learner on Weighted Fuzzy Approximation Spaces for Class Imbalanced and Hybrid Data
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Fuzzy preference based rough sets
Information Sciences: an International Journal
Robust fuzzy rough classifiers
Fuzzy Sets and Systems
Rule learning for classification based on neighborhood covering reduction
Information Sciences: an International Journal
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules, average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.