The Strength of Weak Learnability
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Transductive Methods for the Distributed Ensemble Classification Problem
Neural Computation
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
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Ensemble of diversely trained support vector machines for protein fold recognition
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Protein fold recognition using segmentation-based feature extraction model
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
| Hi-index | 0.00 |
Prediction of tertiary structure of protein from its primary structure (amino acid sequence of protein) without relying on sequential similarity is a challenging task for bioinformatics and biological science. The protein fold prediction problem can be expressed as a prediction problem that can be solved by machine learning techniques. In this paper, a new method based on ensemble of five classifiers (Naïve Bayes, Multi Layer Perceptron (MLP), Support Vector Machine (SVM), LogitBoost and AdaBoost.M1) is proposed for the protein fold prediction problem. The dataset used in this experiment is from the standard dataset provided by Ding and Dubchak. Experimental results show that the proposed method enhanced the prediction accuracy up to 64% on an independent test dataset, which is the highest prediction accuracy in compare with other methods proposed by the works have done by literature.