C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Machine Learning
Protein Folding Class Predictor for SCOP: Approach Based on Global Descriptors
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Effectiveness of Rotation Forest in Meta-learning Based Gene Expression Classification
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
A framework for predicting proteins 3D structures
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
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
Protein fold recognition with adaptive local hyperplane algorithm
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
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
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Evaluating data mining algorithms using molecular dynamics trajectories
International Journal of Data Mining and Bioinformatics
Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition
International Journal of Data Mining and Bioinformatics
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Recent advancement in the pattern recognition field has driven many classification algorithms being implemented to tackle protein fold prediction problem. In this paper, a newly introduced method called Rotation Forest for building ensemble of classifiers based on bootstrap sampling and feature extraction is implemented and applied to challenge this problem. The Rotation Forest is a straight forward extension of bagging algorithms which aims to promote diversity within the ensemble through feature extraction by using Principle Component Analysis (PCA). We compare the performance of the employed method with other Meta classifiers that are based on boosting and bagging algorithms, such as: AdaBoost.M1, LogitBoost, Bagging and Random Forest. Experimental results show that the Rotation Forest enhanced the protein folding prediction accuracy better than the other applied Meta classifiers, as well as the previous works found in the literature.