Genetic Programming and Evolvable Machines
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
An adaptive binary PSO to learn bayesian classifier for prognostic modeling of metabolic syndrome
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
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. 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. 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 biological data sets. The effectiveness of this algorithm is demonstrated on various biological and medical data sets.