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CI
2004
171views more  CI 2004»
13 years 4 months ago
A Multiple Resampling Method for Learning from Imbalanced Data Sets
Andrew Estabrooks, Taeho Jo, Nathalie Japkowicz
FSKD
2008
Springer
174views Fuzzy Logic» more  FSKD 2008»
13 years 6 months ago
A Hybrid Re-sampling Method for SVM Learning from Imbalanced Data Sets
Support Vector Machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the pr...
Peng Li, Pei-Li Qiao, Yuan-Chao Liu
RSCTC
2010
Springer
142views Fuzzy Logic» more  RSCTC 2010»
13 years 2 months ago
Learning from Imbalanced Data in Presence of Noisy and Borderline Examples
In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of ...
Krystyna Napierala, Jerzy Stefanowski, Szymon Wilk
CIDM
2009
IEEE
13 years 11 months ago
Diversity analysis on imbalanced data sets by using ensemble models
— Many real-world applications have problems when learning from imbalanced data sets, such as medical diagnosis, fraud detection, and text classification. Very few minority clas...
Shuo Wang, Xin Yao
DMIN
2007
226views Data Mining» more  DMIN 2007»
13 years 6 months ago
Generative Oversampling for Mining Imbalanced Datasets
— One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, d...
Alexander Liu, Joydeep Ghosh, Cheryl Martin