Fusion of classifiers for protein fold recognition

Predicting the three-dimensional structure of a protein from its amino acid sequence is an important problem in bioinformatics and a challenging task for machine learning algorithms. Given (numerical) features, one of the existing machine learning techniques can be then applied to learn and classify proteins represented by these features. We show that combining Fisher's linear classifier and K-Local Hyperplane Distance Nearest Neighbor we obtain an error rate lower than previously published in the literature.