Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Quantifying trends accurately despite classifier error and class imbalance
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pragmatic text mining: minimizing human effort to quantify many issues in call logs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Quantifying counts and costs via classification
Data Mining and Knowledge Discovery
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
Efficient AUC Maximization with Regularized Least-Squares
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Improving Classification under Changes in Class and Within-Class Distributions
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Document classification for mining host pathogen protein-protein interactions
Artificial Intelligence in Medicine
Computational Statistics & Data Analysis
A unifying view on dataset shift in classification
Pattern Recognition
Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis
ROC analysis of classifiers in machine learning: A survey
Intelligent Data Analysis
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In an article in this issue, Webb and Ting criticize ROC analysis for its inability to handle certain changes in class distributions. They imply that the ability of ROC graphs to depict performance in the face of changing class distributions has been overstated. In this editorial response, we describe two general types of domains and argue that Webb and Ting's concerns apply primarily to only one of them. Furthermore, we show that there are interesting real-world domains of the second type, in which ROC analysis may be expected to hold in the face of changing class distributions.