An efficient image pattern recognition system using an evolutionary search strategy
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Relabeling algorithm for retrieval of noisy instances and improving prediction quality
Computers in Biology and Medicine
On supporting identification in a hand-based biometric framework
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Advances in detecting parkinson's disease
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Empirical metabolite identification via GA feature selection and Bayes classification
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
On the evolutionary optimization of k-NN by label-dependent feature weighting
Pattern Recognition Letters
A regularization framework in polar coordinates for transductive learning in networked data
Information Sciences: an International Journal
A novel classification learning framework based on estimation of distribution algorithms
International Journal of Computing Science and Mathematics
Information Sciences: an International Journal
Detection of protein conformation defects from fluorescence microscopy images
Engineering Applications of Artificial Intelligence
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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A key element of bioinformatics research is the extraction of meaningful information from large experimental data sets. Various approaches, including statistical and graph theoretical methods, data mining, and computational pattern recognition, have been applied to this task with varying degrees of success. Using a novel classifier based on the Bayes discriminant function, we present a hybrid algorithm that employs feature selection and extraction to isolate salient features from large medical and other biological data sets. We have previously shown that a genetic algorithm coupled with a k-nearest-neighbors classifier performs well in extracting information about protein-water binding from X-ray crystallographic protein structure data. The effectiveness of the hybrid EC-Bayes classifier is demonstrated to distinguish the features of this data set that are the most statistically relevant and to weight these features appropriately to aid in the prediction of solvation sites.