Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Classification for Imprecise Environments
Machine Learning
An Empirical Study of MetaCost Using Boosting Algorithms
ECML '00 Proceedings of the 11th European Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparative Study of Cost-Sensitive Boosting Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Repechage Bootstrap Aggregating for Misclassification Cost Reduction
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Stacking for Misclassification Cost Performance
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Boosting Trees for Cost-Sensitive Classifications
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Stacked generalization: when does it work?
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
Issues in stacked generalization
Journal of Artificial Intelligence Research
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This paper investigates the use, for the task of classifier learning in the presence of misclassification costs, of some gradient descent style leveraging approaches to classifier learning: Schapire and Singer's AdaBoost.MH and AdaBoost.MR [16], and Collins et al's multiclass logistic regression method [4], and some modifications that retain the gradient descent style approach. Decision trees and stumps are used as the underlying base classifiers, learned from modified versions of Quinlan's C4.5 [15]. Experiments are reported comparing the performance, in terms of average cost, of the modified methods to that of the originals, and to the previously suggested "Cost Boosting" methods of Ting and Zheng [21] and Ting [18], which also use decision trees based upon modified C4.5 code, but do not have an interpretation in the gradient descent framework. While some of the modifications improve upon the originals in terms of cost performance for both trees and stumps, the comparison with tree-based Cost Boosting suggests that out of the methods first experimented with here, it is one based on stumps that has the most promise.