On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Data fusion in robotics and machine intelligence
Data fusion in robotics and machine intelligence
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
A neural network based multi-classifier system for gene identification in DNA sequences
Neural Computing and Applications
A new expertness index for assessment of secondary structure prediction engines
Computational Biology and Chemistry
Protein secondary structure classifiers fusion using OWA
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Use of fuzzy-logic-inspired features to improve bacterialrecognition through classifier fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
Learning to filter spam emails: An ensemble learning approach
International Journal of Hybrid Intelligent Systems
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Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers' outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outputs a unique structure for every input residue. We have used the confusion matrix of each protein secondary structure classifier, which is representative of classifiers' expertness, as a general reusable pattern for converting its simple class-label assignment to class-preference score. The results obtained using several classifier fusion operators have been compared, on some standard datasets from the EVA server, with simple majority voting and with the results provided by the individual classifiers. The comparative analysis showed that the Choquet fuzzy integral operator had the highest improvement with respect to accuracy, multi-class sensitivity and specificity criteria over both the best performing individual classifier and the other fusion operators, while all of the classifier fusion techniques yielded some improvements too.