Bayes Error Estimation Using Parzen and k-NN Procedures
IEEE Transactions on Pattern Analysis and Machine Intelligence
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Self-organization and associative memory: 3rd edition
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Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extensions of the TOPSIS for group decision-making under fuzzy environment
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On combining classifiers using sum and product rules
Pattern Recognition Letters
Experiments in colour texture analysis
Pattern Recognition Letters
Fusion of classifiers with fuzzy integrals
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Fuzzy Approach to Texture Segmentation
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Automatic texture feature selection for image pixel classification
Pattern Recognition
Advances of Research in Fuzzy Integral for Classifiers' fusion
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Dynamic and static weighting in classifier fusion
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
CLICOM: Cliques for combining multiple clusterings
Expert Systems with Applications: An International Journal
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
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This paper presents a new unsupervised hybrid classifier that combines several base classifiers through a fuzzy multicriteria decision making (MCDM) approach. The base classifiers are: fuzzy clustering, parametric and non-parametric Bayesian approaches, self-organizing feature maps and two versions of learning vector quantization. During the learning phase different partitions are established until a valid partition is found. The partitioning and validation are two automatic processes based on validation measurements. These measures allow computing the competences of each base classifier which are mapped as the weights to be used during the decision process through the MCDM. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the competences make the main contributions of this paper. Although the method is designed for six classifiers it can be extended for a greater number of classifiers. The method is applied for classifying textures in natural images. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods.