We address the issue of binary classification from positive and unlabeled data (PU classification) with a selection bias in the positive data. During the observation process, (i) a sample is exposed to a user, (ii) the user then returns the label for the exposed sample, and (iii) we however can only observe the positive samples. Therefore, the positive labels that we observe are a combination of both the exposure and the labeling, which creates a selection bias problem for the observed positive samples. This scenario represents a conceptual framework for many practical applications, such as recommender systems, which we refer to as ``learning from positive, unlabeled, and exposure data'' (PUE classification). To tackle this problem, we initially assume access to data with exposure labels. Then, we propose a method to identify the function of interest using a strong ignorability assumption and develop an ``Automatic Debiased PUE'' (ADPUE) learning method. This algorithm directly debiases the selection bias without requiring intermediate estimates, such as the propensity score, which is necessary for other learning methods. Through experiments, we demonstrate that our approach outperforms traditional PU learning methods on various semi-synthetic datasets.
翻译:我们从正和未贴标签的数据(PU分类)中处理二进制分类问题,在正数据中存在选择偏差。在观察过程中,(一) 样本暴露给用户,(二) 用户然后返回曝光样本的标签,(三) 我们只能观察正样。因此,我们观察到的积极标签是暴露和标签的结合,对观察到的正样样本造成了选择偏差问题。这个假设是许多实际应用的概念框架,例如建议系统,我们称之为“从正、未贴标签和暴露数据中学习”(PUE分类) 。为了解决这个问题,我们最初假设接触暴露样本的数据。然后,我们提出一种方法,用强烈的忽略假设来确定利息的函数,并开发“自动脱色化 PUE' (ADPUE) 的学习方法。这种算法直接贬低选择偏差,而不需要中间估计,例如偏差分,这是其他学习方法所必需的。我们通过实验,展示了我们的方法超越了各种传统方法。</s>