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PR
2007
112views more  PR 2007»
13 years 11 months ago
The effect of imbalanced data sets on LDA: A theoretical and empirical analysis
This paper demonstrates that the imbalanced data sets have a negative effect on the performance of LDA theoretically. This theoretical analysis is confirmed by the experimental r...
Jigang Xie, ZhengDing Qiu
FSKD
2008
Springer
174views Fuzzy Logic» more  FSKD 2008»
14 years 15 days 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
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
KES
2004
Springer
14 years 4 months ago
A Comparison of Two Approaches to Data Mining from Imbalanced Data
Our objective is a comparison of two data mining approaches to dealing with imbalanced data sets. The first approach is based on saving the original rule set, induced by the LEM2 ...
Jerzy W. Grzymala-Busse, Jerzy Stefanowski, Szymon...
ICIC
2005
Springer
14 years 5 months ago
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning
In recent years, mining with imbalanced data sets receives more and more attentions in both theoretical and practical aspects. This paper introduces the importance of imbalanced da...
Hui Han, Wenyuan Wang, Binghuan Mao
ICPR
2008
IEEE
14 years 5 months ago
A supervised learning approach for imbalanced data sets
This paper presents a new learning approach for pattern classification applications involving imbalanced data sets. In this approach, a clustering technique is employed to resamp...
Giang Hoang Nguyen, Abdesselam Bouzerdoum, Son Lam...