C4.5: programs for machine learning
C4.5: programs for machine learning
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Class Probability Estimation and Cost-Sensitive Classification Decisions
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
A Fully Distributed Framework for Cost-Sensitive Data Mining
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Policy mining: learning decision policies from fixed sets of data
Policy mining: learning decision policies from fixed sets of data
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Inducing a marketing strategy for a new pet insurance company using decision trees
Expert Systems with Applications: An International Journal
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
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This paper proposes a framework for cost-sensitive classification under a generalized cost function. By combining decision trees with sequential binary programming, we can handle unequal misclassification costs, constrained classification, and complex objective functions that other methods cannot. Our approach has two main contributions. First, it provides a new method for cost-sensitive classification that outperforms a traditional, accuracy-based method and some current cost-sensitive approaches. Second, and more important, our approach can handle a generalized cost function, instead of the simpler misclassification cost matrix to which other approaches are limited.