Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
Integrated Sensing and Processing Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
The development of fuzzy decision trees in the framework of Axiomatic Fuzzy Set logic
Applied Soft Computing
Engineering Applications of Artificial Intelligence
An Improved Cluster Oriented Fuzzy Decision Trees
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Why fuzzy decision trees are good rankers
IEEE Transactions on Fuzzy Systems
Fuzzy sets in machine learning and data mining
Applied Soft Computing
An efficient fuzzy-rough attribute reduction approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees
Expert Systems with Applications: An International Journal
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
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In this study, we discuss the use of fuzzy sets regarded as a well-rounded algorithmic vehicle in the construction of decision trees. The concept of fuzzy granulation realized via context-based clustering is aimed at the quantization (discretization) of continuous attributes as well as handling continuous classes encountered in classification problems. Two detailed experimental studies are presented concerning well-known data sets available on the Web