Abstract
In this paper, we present a method for the filteriNg of ovEr-sampled dAta using non-cooperaTive gamE theoRy (NEATER) to address the imbalanced data problem. Specifically, the problem is formulated as a non-cooperative game where all the data are players and the goal is to uniformly and consistently label all of the synthetic data created by any over-sampling technique. The proposed algorithm does not require any prior assumptions and selects representative synthetic instances while generating a very small number of noisy data. We present extensive experimental results over a large collection of datasets using three different classifiers to demonstrate the advantages of our method.
Original language | English (US) |
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Pages (from-to) | 3301-3322 |
Number of pages | 22 |
Journal | Soft Computing |
Volume | 19 |
Issue number | 11 |
DOIs | |
State | Published - Nov 24 2015 |
Keywords
- Classification
- Imbalanced data
- Sampling
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Geometry and Topology