C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
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
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein Fold Recognition using a Structural Hidden Markov Model
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Probabilistic multi-class multi-kernel learning
Bioinformatics
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Improving the protein fold recognition accuracy of a reduced state-space hidden Markov model
Computers in Biology and Medicine
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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
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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Protein fold prediction problem is considered as one of the most challenging tasks for molecular biology and one of the biggest unsolved problems for science. Recently, varieties of classification approaches have been proposed to solve this problem. In this study, a fusion of heterogeneous Meta classifiers namely: LogitBoost, Random Forest, and Rotation Forest is proposed to solve this problem. The proposed approach aims at enhancing the protein fold prediction accuracy by enforcing diversity among its individual members by employing divers and accurate base classifiers. Employed classifiers combined using five different algebraic combiners (combinational policies) namely: Majority voting, Maximum of Probability, Minimum of Probability, Product of Probability, and Average of probability. Our experimental results show that our proposed approach enhances the protein fold prediction accuracy using Ding and Dubchak's dataset and Dubchak et al.'s feature set better than the previous works found in the literature.