Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Fuzzy Sets and Systems
Instance-Based Learning Algorithms
Machine Learning
Fuzzy complex analysis I: differentiation
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Fuzzy complex analysis II: integration
Fuzzy Sets and Systems
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Pattern Recognition Letters
Some remarks for fuzzy complex analysis
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Generalized Lebesgue integrals of fuzzy complex valued functions
Fuzzy Sets and Systems - Mathematics
Document zone content classification and its performance evaluation
Pattern Recognition
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
A New Approach to Fuzzy-Rough Nearest Neighbour Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
The theoretical fundamentals of learning theory based on fuzzy complex random samples
Fuzzy Sets and Systems
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A new extension of fuzzy sets using rough sets: R-fuzzy sets
Information Sciences: an International Journal
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
A novel framework of fuzzy complex numbers and its application to compositional modelling
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
A Distance Measure Approach to Exploring the Rough Set Boundary Region for Attribute Reduction
IEEE Transactions on Knowledge and Data Engineering
Fuzzy-rough approaches for mammographic risk analysis
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
EVIDENCE DIRECTED GENERATION OF PLAUSIBLE CRIME SCENARIOS WITH IDENTITY RESOLUTION
Applied Artificial Intelligence
IEEE Transactions on Fuzzy Systems
Nearest-neighbor guided evaluation of data reliability and its applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Induced ordered weighted averaging operators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy interpolative reasoning via scale and move transformations
IEEE Transactions on Fuzzy Systems
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There are a variety of measures to describe classification performance with respect to different criteria and they are often represented by numerical values. Psychologists have commented that human beings can only reasonably manage to process seven or-so items of information at any one time. Hence, selecting the best classifier amongst a number of alternatives whose performances are represented by similar numerical values is a difficult problem faced by end users. To alleviate such difficulty, this paper presents a new method of linguistic evaluation of classifiers performance. In particular, an innovative notion of fuzzy complex numbers (FCNs) is developed in an effort to represent and aggregate different evaluation measures conjunctively without necessarily integrating them. Such an approach well maintains the underlying semantics of different evaluation measures, thereby ensuring that the resulting ranking scores are readily interpretable and the inference easily explainable. The utility and applicability of this research are illustrated by means of an experiment which evaluates the performance of 16 classifiers using different benchmark datasets. The effectiveness of the proposed approach is compared to conventional statistical approach. Experimental results show that the FCN-based performance evaluation provides an intuitively reliable and consistent means in assisting end users to make informed choices of available classifiers.