C4.5: programs for machine learning
C4.5: programs for machine learning
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
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Exploiting the Cost (In)sensitivity of Decision Tree Splitting Criteria
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
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
Budgeted learning of nailve-bayes classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
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In this paper, we propose a new and general preprocessor algorithm, called CSRoulette, which converts any cost-insensitive classification algorithms into cost-sensitive ones. CSRouletteis based on cost proportional roulette sampling technique (called CPRSin short). CSRouletteis closely related to Costing, another cost-sensitive meta-learning algorithm, which is based on rejection sampling. Unlike rejection sampling which produces smaller samples, CPRScan generate different size samples. To further improve its performance, we apply ensemble (bagging) on CPRS; the resulting algorithm is called CSRoulette. Our experiments show that CSRouletteoutperforms Costing and other meta-learning methods in most datasets tested. In addition, we investigate the effect of various sample sizes and conclude that reduced sample sizes (as in rejection sampling) cannot be compensated by increasing the number of bagging iterations.