Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem
Top Cited Papers
- 1 January 2009
- book chapter
- Published by Springer Nature
- p. 475-482
Abstract
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This publication has 12 references indexed in Scilit:
- Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning2005
- EditorialACM SIGKDD Explorations Newsletter, 2004
- Class Imbalances versus Class Overlapping: An Analysis of a Learning System Behavior2004
- Using artificial anomalies to detect unknown and known network intrusions2002
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods2000
- MetaCost1999
- The use of the area under the ROC curve in the evaluation of machine learning algorithmsPattern Recognition, 1997
- Reducing Misclassification Costs1994
- Heterogeneous Uncertainty Sampling for Supervised Learning1994
- The relationship between Recall and PrecisionJournal of the American Society for Information Science, 1994