A New Neuro-Fuzzy Classifier with Application to On-Line Face Detection and Recognition
Journal of VLSI Signal Processing Systems
A Strategy of Dynamic Reasoning in Knowledge-Based System with Fuzzy Production Rules
Journal of Intelligent Information Systems
Neural Network Based Classifers for a Vast Amount of Data
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Granularity and specificity in fuzzy rule-based systems
Granular computing
Neuro-Fuzzy Approach to the Segmentation of Psoriasis Images
Journal of VLSI Signal Processing Systems
Intelligent data analysis
Pattern Recognition Letters
Clustering Irregular Shapes Using High-Order Neurons
Neural Computation
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
EURASIP Journal on Advances in Signal Processing
Designing of classifiers based on immune principles and fuzzy rules
Information Sciences: an International Journal
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
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
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Improved parameter tuning algorithms for fuzzy classifiers
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
Vehicles license plate recognition based on line scanning of digital image
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
A fuzzy rule-based classification system using interval type-2 fuzzy sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Evolutionary design of fuzzy classifiers using information granules
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Design of t–s fuzzy classifier via linear matrix inequality approach
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Design of fuzzy rule-based classifier: pruning and learning
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A coevolutionary approach to optimize class boundaries for multidimensional classification problems
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer
International Journal of Fuzzy System Applications
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm
Computer Methods and Programs in Biomedicine
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
Information Sciences: an International Journal
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In this paper, we discuss a fuzzy classifier with ellipsoidal regions which has a learning capability. First, we divide the training data for each class into several clusters. Then, for each cluster, we define a fuzzy rule with an ellipsoidal region around a cluster center. Using the training data for each cluster, we calculate the center and the covariance matrix of the ellipsoidal region for the cluster. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. We evaluate our method using the Fisher iris data, numeral data of vehicle license plates, thyroid data, and blood cell data. The recognition rates (except for the thyroid data) of our classifier are comparable to the maximum recognition rates of the multilayered neural network classifier and the training times (except for the iris data) are two to three orders of magnitude shorter