Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Combining Classifiers by Constructive Induction
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Experiments in Meta-level Learning with ILP
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Stacking with an Extended Set of Meta-level Attributes and MLR
ECML '02 Proceedings of the 13th European Conference on Machine Learning
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Learning Classification RBF Networks by Boosting
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Stacking with Multi-response Model Trees
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A meta learning approach: classification by cluster analysis
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Reranking for stacking ensemble learning
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Classification by cluster analysis: a new meta-learning based approach
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Local Feature Selection with Dynamic Integration of Classifiers
Fundamenta Informaticae - Intelligent Systems
Hybrid intelligent systems for predicting software reliability
Applied Soft Computing
Applying Ant Colony Optimization to configuring stacking ensembles for data mining
Expert Systems with Applications: An International Journal
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The paper introduces meta decision trees (MDTs), a novel method for combining multiple models. Instead of giving a prediction, MDT leaves specify which model should be used to obtain a prediction. We present an algorithm for learning MDTs based on the C4.5 algorithm for learning ordinary decision trees (ODTs). An extensive experimental evaluation of the new algorithm is performed on twenty-one data sets, combining models generated by five learning algorithms: two algorithms for learning decision trees, a rule learning algorithm, a nearest neighbor algorithm and a naive Bayes algorithm. In terms of performance, MDTs combine models better than voting and stacking with ODTs. In addition, MDTs are much more concise than ODTs used for stacking and are thus a step towards comprehensible combination of multiple models.