A review and comparison of six reasoning methods
Fuzzy Sets and Systems
A clustering algorithm for fuzzy model identification
Fuzzy Sets and Systems
Fuzzy system modeling in pharmacology: an improved algorithm
Fuzzy Sets and Systems - Fuzzy models
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Fuzzy functions with support vector machines
Information Sciences: an International Journal
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
A New Classifier Design with Fuzzy Functions
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Failure prediction in the Russian bank sector with logit and trait recognition models
Expert Systems with Applications: An International Journal
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
An empirical risk functional to improve learning in a neuro-fuzzy classifier
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
A new fuzzy support vector machine to evaluate credit risk
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
FCMAC-Yager: A Novel Yager-Inference-Scheme-Based Fuzzy CMAC
IEEE Transactions on Neural Networks
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
A generic methodology for developing fuzzy decision models
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
Hi-index | 12.06 |
In building an approximate fuzzy classifier system, significant effort is laid on estimation and fine-tuning of fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within fuzzy rules. In this paper, a robust method, improved fuzzy classifier functions (IFCF) design is proposed for two-class pattern recognition problems. A supervised hybrid improved fuzzy clustering for classification (IFC-C) algorithm is implemented for structure identification. IFC-C algorithm is based on a dual optimization method, which yields simultaneous estimates of the parameters of c-classification functions together with fuzzy c partitioning of dataset based on a distance measure. The merit of novel IFCF is that the information on natural grouping of data samples i.e., the membership values, are utilized as additional predictors of each fuzzy classifier function to improve accuracy of system model. Improved fuzzy classifier functions are approximated using statistical and soft computing approaches. A new semi-non-parametric inference mechanism is implemented for reasoning. The experimental results of the new modeling approach indicate that the new IFCF is a promising method for two-class pattern recognition problems.