Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Ensemble-based noise detection: noise ranking and visual performance evaluation
Data Mining and Knowledge Discovery
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Noise filtering is usually used in data preprocessing to improve the accuracy of induced classifiers. Our goal is different: we aim at detecting noisy instances to be inspected by the domain expert in the phase of data understanding. Consequently, our noise detection algorithms should have high precision of class noise detection, where the precision-recall trade-off is modeled using the F-measure. New variants of class noise detection algorithms have been developed, including the high agreement random forest filter which ensures very high precision of identified erroneous data instances.