caa a22 4500 605468702 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s00500-014-1355-0 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1355-0 Effect of simple ensemble methods on protein secondary structure prediction [Elektronische Daten] [Hafida Bouziane, Belhadri Messabih, Abdallah Chouarfia] Ensemble methods for building improved classifier models have been an important topic in machine learning, pattern recognition and data mining areas, where they have shown great promise. They boast a robustness that has spearheaded their application in many practical classification problems, especially when there is a significant diversity among the ensemble members. Actually, they replace traditional machine learning techniques in many applications and special attention has been devoted to them as a mean to improve the prediction accuracy for problems of high complexity. Several combination rules have been investigated in this context. However, it is claimed that no rule is always better than others for designing an optimal decision. The present study evaluates the performance of two different ensemble methods for protein secondary structure prediction. We focus on weighted opinions pooling and the most common aggregation rules for decisions inference. The ensemble members are accurate protein secondary structure single model predictors namely, Multi-Class Support Vector Machines and Artificial Neural Networks. Experiments are carried out using cross-validation tests on RS126 and CB513 benchmark datasets. Our results clearly confirm that ensembles are more accurate than a single model and the experimental comparison of the investigated ensemble schemes demonstrates that the newly introduced rule called Exponential Opinion Pool competes well against state-of-the-art fixed rules, especially the sum rule which in some cases is able to achieve better performance. Springer-Verlag Berlin Heidelberg, 2014 Ensemble methods nationallicence Simple aggregation rules nationallicence Weighted opinions pooling nationallicence Protein secondary structure prediction nationallicence ANN : Artificial Neural Networks nationallicence BLAST : Basic Local Alignment Search Tool nationallicence BLOSUM : BLOck SUbstitution Matrix nationallicence ExpOP : Exponential Opinion Pool nationallicence FNN : Feed-Forward Neural Network nationallicence IFS : Ideal fold selection nationallicence LinOP : Linear Opinion Pool nationallicence LogOP : Logarithm Opinion Pool nationallicence MLP : Multi-Layer Perceptron nationallicence M-SVM : Multi-Class Support Vector Machines nationallicence MV : Majority vote nationallicence PSI-BLAST : Position-Specific Iterative BLAST nationallicence PSSP : Protein secondary structure prediction nationallicence RBFNN : Radial Basis Function Neural Network nationallicence SVM : Support Vector Machines nationallicence WMax : Weighted Max nationallicence WMin : Weighted Min nationallicence Bouziane Hafida Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut Messabih Belhadri Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut Chouarfia Abdallah Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1663-1678 1432-7643 19:6<1663 2015 19 500 https://doi.org/10.1007/s00500-014-1355-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1355-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bouziane Hafida Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut NATIONALLICENCE 700 1- Messabih Belhadri Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut NATIONALLICENCE 700 1- Chouarfia Abdallah Department of Computer Science, USTO-MB University, BP 1505, El M'naouer, Oran, Algeria aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/6(2015-06-01), 1663-1678 1432-7643 19:6<1663 2015 19 500