title: A novel hierarchical ensemble classifier for protein fold recognition.
abstract: The ensemble classifier plays a critical role in protein fold recognition. In this article, a novel hierarchical ensemble classifier named GAOEC (Genetic-Algorithm Optimized Ensemble Classifier) is presented and it can be constructed in the following steps. First, a novel optimized classifier named GAET-KNN (Genetic-Algorithm Evidence-Theoretic K Nearest Neighbors) is proposed as a component classifier. Second, six component classifiers in the first layer are used to get a potential class index for every query protein. Third, according to the results of the first layer, every component classifier in the second layer generates a 27-dimension vector whose elements represent the confidence degrees of 27-folds. Finally, genetic algorithm is used for generating weights for the outputs of the second layer to get the final classification result. The standard percentage accuracy of GAOEC is 64.7% on a widely used benchmark dataset, where the proteins in the testing set have less than 35% identity with those in the training set.
authors.affiliation: Information Engineering College, Xiangtan University, Xiangtan 411105, Hunan, PR China.
authors.forename: Xia
authors.initials: X
authors.lastname: Guo
authors.forename: Xieping
authors.initials: X
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D010363:Pattern Recognition, Automated / Q000379:methods*; D017510:Protein Folding