Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability
Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability
复制标题
使用倾斜标记后验概率对正数据和有偏差的负数据进行分类
DOI：
10.1162/neco_a_01580
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发表时间：
2023
影响因子：
2.9
通讯作者：
Matsui Hidetoshi
中科院分区：
文献类型：
--
作者：
Watanabe Shotaro;Matsui Hidetoshi
二元分类问题的情况是，在其中一个类中只观察到有偏差的数据。在这篇文章中，我们提出了一种新的方法来解决正偏负（PbN）分类问题，这是一种弱监督学习方法，从有偏观测值的正数据和负数据中学习二分类器。我们采用了一种方法来纠正由于偏斜置信度造成的负面影响，这是由观察到的数据为正的后验概率表示的。这减少了数据被标记后验概率的失真，这对于PbN分类问题的经验风险最小化是必要的。通过综合和基准数据实验验证了该方法的有效性。
The binary classification problem has a situation where only biased data are observed in one of the classes. In this letter, we propose a new method to approach the positive and biased negative (PbN) classification problem, which is a weakly supervised learning method to learn a binary classifier from positive data and negative data with biased observations. We incorporate a method to correct the negative influence due to a skewed confidence, which is represented by the posterior probability that the observed data are positive. This reduces the distortion of the posterior probability that the data are labeled, which is necessary for the empirical risk minimization of the PbN classification problem. We verified the effectiveness of the proposed method by synthetic and benchmark data experiments.