Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
How to Make Stacking Better and Faster While Also Taking Care of an Unknown Weakness
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Numerous machine learning problems involve an investigation of relationships between features in heterogeneous datasets, where different classifier can be more appropriate for different regions. We propose a technique of localized cascade generalization of weak classifiers. This technique identifies local regions having similar characteristics and then uses the cascade generalization of local experts to describe the relationship between the data characteristics and the target class. We performed a comparison with other well known combining methods using weak classifiers as base-learners, on standard benchmark datasets and the proposed technique was more accurate.