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
Error reduction through learning multiple descriptions
Machine Learning
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Machine Learning
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
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
iBoost: Boosting Using an i nstance-Based Exponential Weighting Scheme
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Stacking with Multi-response Model Trees
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Introduction to the Special Issue on Meta-Learning
Machine Learning
Machine Learning
Automatic identification of music performers with learning ensembles
Artificial Intelligence
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
Constructing ensembles of symbolic classifiers
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
iBoost: Boosting using an instance-based exponential weighting scheme
International Journal of Hybrid Intelligent Systems
Expert Systems with Applications: An International Journal
Selective generation of training examples in active meta-learning
International Journal of Hybrid Intelligent Systems - HIS 2007
Neural Network Ensembles for Classification Problems Using Multiobjective Genetic Algorithms
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Methods and algorithms of collective recognition
Automation and Remote Control
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
Boosting k-nearest neighbor classifier by means of input space projection
Expert Systems with Applications: An International Journal
Supervised Machine Learning: A Review of Classification Techniques
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
Unsupervised Hierarchical Weighted Multi-segmenter
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Combining learning constraints and numerical regression
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automatic identification of music performers with learning ensembles
Artificial Intelligence
IEEE Transactions on Neural Networks
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An improved random subspace method and its application to EEG signal classification
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Building classifier ensembles for automatic sports classification
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Control of complex machines for meta-learning in computational intelligence
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
An ensemble method for incremental classification in stationary and non-stationary environments
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Financial distress prediction using support vector machines: Ensemble vs. individual
Applied Soft Computing
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
A distributed hebb neural network for network anomaly detection
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
| Hi-index | 0.01 |
The paper introduces meta decision trees (MDTs), a novel method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier 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 classifiers 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, stacking with MDTs combines classifiers better than voting and stacking with ODTs. In addition, the MDTs are much more concise than the ODTs and are thus a step towards comprehensible combination of multiple classifiers. MDTs also perform better than several other approaches to stacking.