Graph-based Semi-supervised Learning Algorithm for Web Page Classification
Graph-based Semi-supervised Learning Algorithm for Web Page Classification
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DOI：
10.1109/isda.2006.253724
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
Rong Liu;Jian-zhong Zhou;Ming Liu
中科院分区：
文献类型：
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作者：
Rong Liu;Jian-zhong Zhou;Ming Liu
许多应用领域，如Web页面分类，没有足够的标记训练样本进行学习。然而，未标记的训练示例很容易获得，但标记的训练示例获得起来相当昂贵。因此，近几年来，半监督学习已经有了大量的研究工作。提出了一种基于图的半监督学习算法，并将其应用于网页分类。我们的算法使用Web页面之间的相似性度量来构建一个k-近邻图。标记和未标记的网页表示为加权图中的节点，边权重编码网页之间的相似性。为了利用未标记数据进行分类，提高分类精度，结合加权方案和网页链接信息计算图的边权值。学习问题，然后制定在标签传播的图形。利用概率矩阵方法和信念传播，标记节点通过未标记节点推出标签。在WebKB数据集上的初步实验表明，该算法能有效地利用未标记数据和已标记数据，提高网页分类的准确率
Many application domains such as Web page classification suffer from not having enough labeled training examples for learning. However, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. As a result, there has been a great deal of work in resent years on semi-supervised learning. This paper proposes a graph-based semi-supervised learning algorithm that is applied to the Web page classification. Our algorithm uses a similarity measure between Web pages to construct a k-nearest neighbor graph. Labeled and unlabeled Web pages are represented as nodes in the weighted graph, with edge weights encoding the similarity between the Web pages. In order to use unlabeled data to help classification and get higher accuracy, edge weights of the graph are computed through combining weighting schemes and link information of Web pages. The learning problem is then formulated in terms of label propagation in the graph. By using probabilistic matrix methods and belief propagation, the labeled nodes push out labels through unlabeled nodes. Our preliminary experiments on the WebKB dataset show that the algorithm in this paper can effectively exploit unlabeled data in addition to labeled ones to get higher accuracy of Web page classification