一个装袋SVM，从正面和未标记的例子中学习
A bagging SVM to learn from positive and unlabeled examples
摘要：
我们考虑的问题，学习一个二元分类器的训练集的积极和未标记的例子，无论是在归纳和转导设置。这个问题，通常被称为\emph{PU learning}，与标准的监督分类问题的不同之处在于训练集中缺乏负样本。它对应于在许多应用中普遍存在的情况，如信息检索或基因排名，当我们已经确定了一组共享特定属性的感兴趣的数据，并且我们希望自动检索在大量且易于获得的未标记数据池中共享相同属性的其他数据时。我们提出了一种概念上简单的方法，类似于装袋，通过将它们转换为一系列的监督二进制分类问题，区分已知的正样本的随机子样本的未标记集，来处理归纳和转导PU学习问题。我们经验性地证明了该方法在模拟数据和真实数据上的相关性，它的性能至少与现有方法一样好，同时速度更快。
英文摘要：
We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as \emph{PU learning}, differs from the standard supervised classification problem by the lack of negative examples in the training set. It corresponds to an ubiquitous situation in many applications such as information retrieval or gene ranking, when we have identified a set of data of interest sharing a particular property, and we wish to automatically retrieve additional data sharing the same property among a large and easily available pool of unlabeled data. We propose a conceptually simple method, akin to bagging, to approach both inductive and transductive PU learning problems, by converting them into series of supervised binary classification problems discriminating the known positive examples from random subsamples of the unlabeled set. We empirically demonstrate the relevance of the method on simulated and real data, where it performs at least as well as existing methods while being faster.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
