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KES
2004
Springer
13 years 10 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...
SDM
2010
SIAM
184views Data Mining» more  SDM 2010»
13 years 6 months ago
A Robust Decision Tree Algorithm for Imbalanced Data Sets
We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are st...
Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V...
DAWAK
2008
Springer
13 years 7 months 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
PKDD
2005
Springer
109views Data Mining» more  PKDD 2005»
13 years 10 months ago
An Imbalanced Data Rule Learner
Imbalanced data learning has recently begun to receive much attention from research and industrial communities as traditional machine learners no longer give satisfactory results. ...
Canh Hao Nguyen, Tu Bao Ho
ICIC
2005
Springer
13 years 10 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