Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence in Medicine
Exploring alternative knowledge representations for protein secondary-structure prediction
International Journal of Data Mining and Bioinformatics
A conditional independence algorithm for learning undirected graphical models
Journal of Computer and System Sciences
A proposed knowledge based approach for solving proteomics issues
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Using classifier fusion techniques for protein secondary structure prediction
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Predicting protein second structure using a novel hybrid method
Expert Systems with Applications: An International Journal
PSSP with dynamic weighted kernel fusion based on SVM-PHGS
Knowledge-Based Systems
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Protein secondary structure classifiers fusion using OWA
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Improving protein secondary structure prediction using a multi-modal BP method
Computers in Biology and Medicine
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi nai@?ve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.