NEATER: filtering of over-sampled data using non-cooperative game theory
NEATER: filtering of over-sampled data using non-cooperative game theory
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DOI：
10.1007/s00500-014-1484-5
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发表时间：
2015-11-01
期刊：
影响因子：
4.1
通讯作者：
Kakadiaris, I. A.
中科院分区：
文献类型：
--
作者：
Almogahed, B. A.;Kakadiaris, I. A.
本文提出了一种利用非合作博弈论（NEATER）对过采样数据进行过滤的方法，以解决数据不平衡问题。具体来说，这个问题被表述为一个非合作博弈，其中所有数据都是玩家，目标是统一和一致地标记由任何过度抽样技术创建的所有合成数据。该算法不需要任何先验假设，在生成极少量噪声数据的同时选择具有代表性的合成实例。我们使用三种不同的分类器在大量数据集上展示了广泛的实验结果，以证明我们的方法的优势。
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.