数据分布估计下基于相似度的PU文本分类方法
PU Text Classification Method Based on Similarity under Data Distribution Estimation
DOI: 10.12677/CSA.2018.85088, PDF,    国家自然科学基金支持
作者: 胡学钢*, 张 路, 李培培：合肥工业大学计算机与信息学院，安徽 合肥
关键词: PU学习；文本分类；多核学习；PU Learning； Text Classification； Multiple Kernel Learning
摘要: 在实际的应用中，由于各种原因通常无法获取已标注的反例数据，这使得传统分类算法失灵，这一类基于正例数据与未标注数据的学习称为PU分类问题。PU问题的关键在于反例样本提取与有效分类器的构建。本文提出算法首先通过评估样本中数据分布情况，采用集成机制从未标注样本中抽取出合适比例可信的正反例数据，其次利用相似度抽取有代表性的正例微簇和反例微簇，在获取足量的正反例样本后，将PU问题转换为二元分类问题，数值实验表明方法的有效性。
Abstract: In actual applications, due to various reasons, it is usually impossible to obtain the marked negative data, which causes the traditional classification algorithm to fail. Based on positive data and unlabeled data learning, it is called the PU classification problem. The key of the PU problem lies in the extraction of negative data and the construction of effective classifiers. The algorithm proposed in this paper firstly evaluates the data distribution in the sample, adopts the integration mechanism to extract positive and negative example data from the unlabeled sample with reasonable proportion, and then uses similarity to extract the representative positive micro-clusters and negative micro-clusters. After sufficient samples of positive and negative samples were obtained, the PU problem was converted to a binary classification problem. Numerical experiments showed the effectiveness of the method.
文章引用：胡学钢, 张路, 李培培. 数据分布估计下基于相似度的PU文本分类方法[J]. 计算机科学与应用, 2018, 8(5): 788-797. https://doi.org/10.12677/CSA.2018.85088

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