Original Contribution: Stacked generalization
Neural Networks
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
On the Accuracy of Meta-learning for Scalable Data Mining
Journal of Intelligent Information Systems
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Meta-Learning by Landmarking Various Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved Dataset Characterisation for Meta-learning
DS '02 Proceedings of the 5th International Conference on Discovery Science
Model selection via meta-learning: a comparative study
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
On Classifier Domains of Competence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Layered concept-learning and dynamically variable bias management
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Prediction of classifier training time including parameter optimization
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Efficient feature size reduction via predictive forward selection
Pattern Recognition
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Information-theoretic measures are suitable to characterize datasets with discrete attributes (or continuous which can be transformed). They can find information that can be decisive in order to analyze the behavior of different learning algorithms with specific datasets. The objective of the work presented in this paper is to study by means of three similar datasets from UCI Repository Machine Learning, the possible reasons for which breast-cancer-wisconsin dataset, in comparison with other 20 datasets, showed in a previous research that Stacking by Meta-Decision Trees (MDT) was significant better than all other multiclassifier models, including Stacking by Multi-Response Linear Regression (MLR). In our experiments the proportion of missing values, among other significant changes in different measure values, provided evidences about the possible origin of the different behaviors presented by these multiclassifier schemes depending on data characteristics.