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
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cost-Guided Class Noise Handling for Effective Cost-Sensitive Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Mining and Knowledge Discovery
Journal of Artificial Intelligence Research
Journal of Data and Information Quality (JDIQ)
Soft fuzzy rough sets for robust feature evaluation and selection
Information Sciences: an International Journal
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An exploration of learning when data is noisy and imbalanced
Intelligent Data Analysis
Robust fuzzy rough classifiers
Fuzzy Sets and Systems
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Recent research in machine learning, data mining, and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the misclassification cost becomes the objective. These methods often assume that their input is quality data without conflict or erroneous values, or the noise impact is trivial, which is seldom the case in real-world environments. In this paper, we propose a Cost-guided Iterative Classification Filter (CICF) to identify noise for effective CS learning. Instead of putting equal weights on handling noise in all classes in existing efforts, CICF puts more emphasis on expensive classes, which makes it attractive in dealing with data sets with a large cost-ratio. Experimental results and comparative studies indicate that the existence of noise may seriously corrupt the performance of the underlying CS learners and by adopting the proposed CICF algorithm, we can significantly reduce the misclassification cost of a CS classifier in noisy environments.