Discrimination aware classification for imbalanced datasets
Discrimination aware classification for imbalanced datasets
复制标题
不平衡数据集的歧视感知分类
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
J. Bailey
中科院分区：
文献类型：
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作者：
G. Ristanoski;Wei Liu;J. Bailey
识别模型的学习问题最近在数据挖掘界受到了关注。已经提出了各种方法和改进的模型，主要的方法是检测的歧视敏感属性。一旦鉴别敏感属性被识别，该方法旨在开发一种策略，该策略将包括来自该属性的有用信息，而不会引起任何额外的鉴别。我们的工作集中在歧视意识分类中经常被忽视的一个方面-不平衡数据集的场景，其中一类的样本数量与另一类不成比例。我们还研究了一种策略，直接减少歧视，是独立的类平衡。我们的实证研究结果表明，需要考虑的歧视意识分类时，我们提出的策略，在克服这些问题的承诺。
The problem of learning a discrimination aware model has recently received attention in the data mining community. Various methods and improved models have been proposed, with the main approach being the detection of a discrimination sensitive attribute. Once the discrimination sensitive attribute is identified, the methods aim to develop a strategy that will include the useful information from that attribute without causing any additional discrimination. Our work focuses on an aspect often overlooked in the discrimination aware classification - the scenario of an imbalanced dataset, where the number of samples from one class is disproportionate to the other. We also investigate a strategy that is directly minimizing discrimination and is independent of the class balance. Our empirical results indicate additional concerns that need to be considered when developing discrimination aware classifiers, and our proposed strategy shows promise in overcoming these concerns.