Random Forests for land cover classification
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Graphical interpretation of the twofold integral and its generalization
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
The Use of Fuzzy t-Conorm Integral for Combining Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Expert Systems with Applications: An International Journal
Real-Time Road Sign Detection Using Fuzzy-Boosting
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Study of Facial Features Combination Using a Novel Adaptive Fuzzy Integral Fusion Model
IEICE - Transactions on Information and Systems
On the moments and distribution of discrete Choquet integrals from continuous distributions
Journal of Computational and Applied Mathematics
Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Online evolutionary context-aware classifier ensemble framework for object recognition
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
International Journal of Approximate Reasoning
Interval Type-2 fuzzy voter design for fault tolerant systems
Information Sciences: an International Journal
Computers in Biology and Medicine
Application of a fuzzy integral for weak classifiers boosting
Pattern Recognition and Image Analysis
Classifier fusion: combination methods for semantic indexing in video content
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Information fusion for local gabor features based frontal face verification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Modeling decisions for artificial intelligence: theory, tools and applications
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Combined classifiers for action recognition
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Scalable fuzzy genetic classifier based on fitness approximation
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
A fuzzy evolutionary framework for combining ensembles
Applied Soft Computing
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling
Computer Speech and Language
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Boosting is recognized as one of the most successful techniques for generating classifier ensembles. Typically, the classifier outputs are combined by the weighted majority vote. The purpose of this study is to demonstrate the advantages of some fuzzy combination methods for ensembles of classifiers designed by Boosting. We ran two-fold cross-validation experiments on six benchmark data sets to compare the fuzzy and nonfuzzy combination methods. On the "fuzzy side" we used the fuzzy integral and the decision templates with different similarity measures. On the "nonfuzzy side" we tried the weighted majority vote as well as simple combiners such as the majority vote, minimum, maximum, average, product, and the Naive-Bayes combination. In our experiments, the fuzzy combination methods performed consistently better than the nonfuzzy methods. The weighted majority vote showed a stable performance, though slightly inferior to the performance of the fuzzy combiners.