Protein Secondary Structure Classifiers Fusion Using OWA
نویسندگان
چکیده
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.
منابع مشابه
Using classifier fusion techniques for protein secondary structure prediction
Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers’ outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outp...
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