Knowledge Discovery in Medical and Biological Datasets using a Hybrid Bayes Classifier/Evolutionary AlgorithmIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AbstractA 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.
Citation InformationMichael L. Raymer, Travis E. Doom, Leslie A. Kuhn and William F. Punch. "Knowledge Discovery in Medical and Biological Datasets using a Hybrid Bayes Classifier/Evolutionary Algorithm" IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Vol. 33 Iss. 5 (2003) p. 802 - 813 ISSN: 10834419
Available at: http://works.bepress.com/michael_raymer/59/