Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
Addressing the Curse of Imbalanced Training Sets: One-Sided Selection
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发表时间：
1997
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通讯作者：
M. Kubát;S. Matwin
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作者：
M. Kubát;S. Matwin
将多数类的例子添加到训练集中可能会对学习者的行为产生不利影响：来自多数类的嘈杂或其他不可靠的例子可能会压倒少数类。本文讨论了从这种不平衡的训练集导出的分类器的效用的评估标准，给出了在这种情况下一些学习者的不良行为的解释，并建议作为解决方案的一个简单的技术称为单侧选择的例子
Adding examples of the majority class to the training set can have a detrimental e(cid:11)ect on the learner's behavior: noisy or otherwise unreliable examples from the majority class can overwhelm the minority class. The paper discusses criteria to evaluate the utility of classi(cid:12)ers induced from such imbalanced training sets, gives explanation of the poor behavior of some learners under these circumstances, and suggests as a solution a simple technique called one-sided selection of examples