Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
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
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Journal of Artificial Intelligence Research
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
The Journal of Machine Learning Research
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A simple methodology for soft cost-sensitive classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Online Classification
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Decision Trees for Mining Data Streams Based on the McDiarmid's Bound
IEEE Transactions on Knowledge and Data Engineering
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Nowadays in many real-world scenarios, high speed data streams are usually with non-uniform misclassification costs and thus call for cost-sensitive classification algorithms of data streams. However, only little literature focuses on this issue. On the other hand, the existing algorithms for cost-sensitive classification can achieve excellent performance in the metric of total misclassification costs, but always lead to obvious reduction of accuracy, which restrains the practical application greatly. In this paper, we present an improved folk theorem. Based on the new theorem, the existing accuracy-based classification algorithm can be converted into soft cost-sensitive one immediately, which allows us to take both accuracy and cost into account. Following the idea of this theorem, the soft-CsGDT algorithm is proposed to process the data streams with non-uniform misclassification costs, which is an expansion of GDT. With both synthetic and real-world datasets, the experimental results show that compared with the cost-sensitive algorithm, the accuracy in our soft-CsGDT is significantly improved, while the total misclassification costs are approximately the same.