On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Ensemble classifier for protein fold pattern recognition
Bioinformatics
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Protein secondary structure classifiers fusion using OWA
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition
International Journal of Data Mining and Bioinformatics
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Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by which the function of a protein whose structure is unknown can be determined, is much more complicated than that of protein structural classes. The classification rates achieved using different methods to solve this problem are not satisfactory and there is an urgent need to improve this classification rate. In this paper, a set of basic classifiers is used where each one is trained in different parameter systems all extracted from a common training dataset. Each individual classifier uses Probabilistic Neural Networks for classification in which the radial basis function parameter is optimized by Particle Swarm Optimization algorithm. Their outcomes are combined thru a weighted voting and Ordered Weighted Averaging (OWA) for final determination of classifying a query protein. The recognition rate achieved is 5-8% higher than the corresponding rates obtained by various existing Neural Networks.