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FLAIRS
2008
14 years 1 months ago
Selecting Minority Examples from Misclassified Data for Over-Sampling
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
Jorge de la Calleja, Olac Fuentes, Jesús Go...
FLAIRS
2007
14 years 1 months ago
A Distance-Based Over-Sampling Method for Learning from Imbalanced Data Sets
Many real-world domains present the problem of imbalanced data sets, where examples of one classes significantly outnumber examples of other classes. This makes learning difficu...
Jorge de la Calleja, Olac Fuentes
JAIR
2002
95views more  JAIR 2002»
13 years 10 months ago
SMOTE: Synthetic Minority Over-sampling Technique
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally repres...
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hal...
DAWAK
2008
Springer
14 years 28 days ago
Selective Pre-processing of Imbalanced Data for Improving Classification Performance
In this paper we discuss problems of constructing classifiers from imbalanced data. We describe a new approach to selective preprocessing of imbalanced data which combines local ov...
Jerzy Stefanowski, Szymon Wilk
TCBB
2011
13 years 6 months ago
Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification
—In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority classes. It is because the trained classifiers are mostly d...
Sangyoon Oh, Min Su Lee, Byoung-Tak Zhang