Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments
Annals of Mathematics and Artificial Intelligence
A reformulation of entropy in the presence of indistinguishability operators
Fuzzy Sets and Systems
Machine Learning
Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Entropies of fuzzy indiscrenibility relation and its operations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Fuzzy reasoning model under quotient space structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Dealing with uncertainty in data mining and information extraction
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
Similarity relations and fuzzy orderings
Information Sciences: an International Journal
Constructing rough decision forests
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Association reducts: a framework for mining multi-attribute dependencies
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Uncertainty representation using fuzzy measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
On the entropy of fuzzy measures
IEEE Transactions on Fuzzy Systems
Constructive granular systems with universal approximation and fast knowledge discovery
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fundamenta Informaticae
Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach
Applied Soft Computing
The information content of fuzzy relations and fuzzy rules
Computers & Mathematics with Applications
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Applying cluster-based fuzzy association rules mining framework into EC environment
Applied Soft Computing
Parameterized attribute reduction with Gaussian kernel based fuzzy rough sets
Information Sciences: an International Journal
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Relations and relation matrices are important concepts in set theory and intelligent computation. Some general uncertainty measures for fuzzy relations are proposed by generalizing Shannon's information entropy. Then, the proposed measures are used to calculate the diversity quantity of multiple classifier systems and the granularity of granulated problem spaces, respectively. As a diversity measure, it is shown that the fusion system whose classifiers are of little similarity produces a great uncertainty quantity, which means that much complementary information is achieved with a diverse multiple classifier system. In granular computing, a ''coarse-fine'' order is introduced for a family of problem spaces with the proposed granularity measures. The problem space that is finely granulated will get a great uncertainty quantity compared with the coarse problem space. Based on the observation, we employ the proposed measure to evaluate the significance of numerical attributes for classification. Each numerical attribute generates a fuzzy similarity relation over the sample space. We compute the condition entropy of a numerical attribute or a set of numerical attribute relative to the decision, where the greater the condition entropy is, the less important the attribute subset is. A forward greedy search algorithm for numerical feature selection is constructed with the proposed measure. Experimental results show that the proposed method presents an efficient and effective solution for numerical feature analysis.