Protein Secondary Structure Classifiers Fusion Using OWA

  • Majid Kazemian
  • Behzad Moshiri
  • Hamid Nikbakht
  • Caro Lucas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)


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.


Confusion Matrix Secondary Structure Prediction Protein Secondary Structure Ordered Weight Average Ordered Weight Average Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Majid Kazemian
    • 1
  • Behzad Moshiri
    • 1
  • Hamid Nikbakht
    • 2
  • Caro Lucas
    • 1
  1. 1.Control and Intelligent Processing Center of Excellence, Electrical and Computer Eng., DepartmentUniversity of TehranTehranIran
  2. 2.Laboratory of Biophysics and Molecular Biology, Institute of Biochemistry and BiophysicsUniversity of TehranTehranIran

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