Protein secondary structure classifiers fusion using OWA

Authors:
Majid Kazemian;Behzad Moshiri;Hamid Nikbakht;Caro Lucas
Affiliations:
Control and Intelligent Processing Center of Excellence, Electrical and Computer Eng., Department, University of Tehran, Tehran, Iran;Control and Intelligent Processing Center of Excellence, Electrical and Computer Eng., Department, University of Tehran, Tehran, Iran;Laboratory of Biophysics and Molecular Biology, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran;Control and Intelligent Processing Center of Excellence, Electrical and Computer Eng., Department, University of Tehran, Tehran, Iran
Venue:
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
Year:
2005

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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 averaging 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.