The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Improved protein fold assignment using support vector machines
International Journal of Bioinformatics Research and Applications
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Performance Analysis of Algorithms for Protein Structure Classification
DEXA '09 Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application
Protein Fold Pattern Recognition Using Bayesian Ensemble of RBF Neural Networks
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Application of classifier fusion for protein fold recognition
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Using rotation forest for protein fold prediction problem: an empirical study
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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In this paper, some methods for ensemble learning of protein fold recognition based on a decision tree DT are compared and contrasted against each other over three datasets taken from the literature. According to previously reported studies, the features of the datasets are divided into some groups. Then, for each of these groups, three ensemble classifiers, namely, random forest, rotation forest and AdaBoost.M1 are employed. Also, some fusion methods are introduced for combining the ensemble classifiers obtained in the previous step. After this step, three classifiers are produced based on the combination of classifiers of types random forest, rotation forest and AdaBoost.M1. Finally, the three different classifiers achieved are combined to make an overall classifier. Experimental results show that the overall classifier obtained by the genetic algorithm GA weighting fusion method, is the best one in comparison to previously applied methods in terms of classification accuracy.