Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
Classification process analysis of bioinformatics data with a support vector fuzzy inference system
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
Efficient and interpretable fuzzy classifiers from data with support vector learning
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Efficient Implementation of SVM Training on Embedded Electronic Systems
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Entropy-based associative classification algorithm for mining manufacturing data
International Journal of Computer Integrated Manufacturing
Reduced-set vector-based interval type-2 fuzzy neural network
WSEAS Transactions on Computers
TS-fuzzy system-based support vector regression
Fuzzy Sets and Systems
Reduced-set vector learning based on hybrid kernels for interval type 2 fuzzy modeling
ICS'08 Proceedings of the 12th WSEAS international conference on Systems
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
On support vector regression machines with linguistic interpretation of the kernel matrix
Fuzzy Sets and Systems
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Information Sciences: an International Journal
A maximizing-discriminability-based self-organizing fuzzy network for classification problems
IEEE Transactions on Fuzzy Systems
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Integrating machine learning in intelligent bioinformatics
WSEAS Transactions on Computers
Information Sciences: an International Journal
Fuzzy chance constrained support vector machine
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Expert Systems with Applications: An International Journal
Intelligent data analysis and model interpretation with spectral analysis fuzzy symbolic modeling
International Journal of Approximate Reasoning
Mining efficient and interpretable fuzzy classifiers from data with support vector learning
ICAI'05/MCBC'05/AMTA'05/MCBE'05 Proceedings of the 6th WSEAS international conference on Automation & information, and 6th WSEAS international conference on mathematics and computers in biology and chemistry, and 6th WSEAS international conference on acoustics and music: theory and applications, and 6th WSEAS international conference on Mathematics and computers in business and economics
T-S fuzzy modeling based on support vector learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Difference-similitude matrix in text classification
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Information Sciences: an International Journal
International Journal of Computational Vision and Robotics
Smooth support vector learning for fuzzy rule-based classification systems
Intelligent Data Analysis
Fuzzy fast classification algorithm with hybrid of ID3 and SVM
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
TS-fuzzy modeling based on ε-insensitive smooth support vector regression
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.