Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Decision Making with Probabilistic Decision Tables
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Fuzzy-rough nearest neighbor algorithms in classification
Fuzzy Sets and Systems
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Attribute selection with fuzzy decision reducts
Information Sciences: an International Journal
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Interval-valued fuzzy-rough feature selection in datasets with missing values
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy-rough nearest neighbour classification
Transactions on rough sets XIII
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
It is estimated that every 20 months or so the amount of information in the world doubles. In the same way, tools that mine knowledge from data must develop to combat this growth. Fuzzy-rough set theory provides a framework for developing such applications in a way that combines the best properties of fuzzy sets and rough sets, in order to handle uncertainty. In this tutorial we will cover the mathematical groundwork required for an understanding of the data mining methods, before looking at some of the key developments in the area, including feature selection and classifier learning..