Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
GA-Based Internet Traffic Classification Technique for QoS Provisioning
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
On learning algorithm selection for classification
Applied Soft Computing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
Bagging is a popular method that could improve the classification accuracy for any unstable learning algorithm. A trial and error classifier feeding with the Bagging algorithm is a regular practice for classification tasks in the machine learning community. In this research we propose a rule based method using well established meta learning approach for unique classifier selection. The generated rules are verified using 113 classification problems with 10 fold cross validation processes. That makes Bagging is a computationally faster algorithm and provides a unique solution for classifier selection.