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...
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...
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...
Imbalanced data learning has recently begun to receive much attention from research and industrial communities as traditional machine learners no longer give satisfactory results. ...
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...