Protein secondary structure classifiers fusion using OWA

Abstract

The combination of classifiers has been proposed as a method to improve the accuracy achieved by a single classifier. In this study, the performances of optimistic and pessimistic ordered weighted averaging1 operators for protein secondary structure classifiers fusion have been investigated. Each secondary structure classifier outputs a unique structure for each input residue. We used confusion matrix of each secondary structure classifier as a general reusable pattern for converting this unique label to measurement level. The results of optimistic and pessimistic OWA operators have been compared with majority voting and five common classifiers used in the fusion process. Using a benchmark set from the EVA server, the results showed a significant improvement in the average Q3 prediction accuracy up to 1.69% toward the best classifier results. © Springer-Verlag Berlin Heidelberg 2005.