Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Experiments on multistrategy learning by meta-learning
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Machine Learning
Mining Very Large Databases with Parallel Processing
Mining Very Large Databases with Parallel Processing
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Constructing Ensembles of Symbolic Classifiers
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
KNOMA: A New Approach for Knowledge Integration
ISCC '06 Proceedings of the 11th IEEE Symposium on Computers and Communications
Methods for Constructing Symbolic Ensembles from Symbolic Classifiers
Proceedings of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005
| Hi-index | 0.00 |
In this paper we compare the performance of KNOMA (Knowledge Mining Approach), a meta-learning approach for integration of rule-based classifiers, based on different rule inducers. Meta-learning approaches use a core learning algorithm for the generation of base classifiers that are further combined into a global one. This approach improves performance and scalability of data mining processes on large datasets. In a previous work we presented KNOMA, a meta-learning approach whose performance was evaluated using RIPPER as its core learning algorithm. Experiments have shown that the performance of KNOMA is comparable to that achieved with Bagging and Boosting. However, meta-learning is generally only sensitive to core algorithms used in the generation of base classifiers. KNOMA is a generic approach and can handle different rule-based inducers, although its advantages, drawbacks and use cases need to be precisely identified. We studied the variation of performance in the approach with base classifiers generated by two rule inducers (C45Rules and RIPPER) and also by C4.5. Interesting behaviors have been noticed in the experiments.