图分类的半监督特征选择

Xiangnan Kong, Philip S. Yu
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引用次数: 122

摘要

近十年来,图的分类问题引起了人们极大的兴趣。目前的图分类研究假设存在大量的标记训练图。然而,在许多应用程序中,图数据的标签非常昂贵或难以获得,而通常有大量未标记的图数据可用。本文研究了图分类的半监督特征选择问题,提出了一种新的解决方案,称为gSSC,以有效地搜索具有标记和未标记图的最优子图特征。与现有的向量空间特征选择方法假设特征集是给定的不同,我们对图数据进行半监督特征选择,并结合子图特征挖掘过程逐步进行。我们推导了一个特征评估准则,名为gSemi,用于估计基于标记和未标记图的子图特征的有用性。然后,我们提出了一种分支定界算法,通过对子图搜索空间进行明智的剪枝来有效地搜索最优子图特征。对几个现实世界任务的实证研究表明,我们的半监督特征选择方法可以有效地提高半监督特征选择的图分类性能,并且通过使用标记和未标记的图修剪子图搜索空间非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-supervised feature selection for graph classification
The problem of graph classification has attracted great interest in the last decade. Current research on graph classification assumes the existence of large amounts of labeled training graphs. However, in many applications, the labels of graph data are very expensive or difficult to obtain, while there are often copious amounts of unlabeled graph data available. In this paper, we study the problem of semi-supervised feature selection for graph classification and propose a novel solution, called gSSC, to efficiently search for optimal subgraph features with labeled and unlabeled graphs. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform semi-supervised feature selection for graph data in a progressive way together with the subgraph feature mining process. We derive a feature evaluation criterion, named gSemi, to estimate the usefulness of subgraph features based upon both labeled and unlabeled graphs. Then we propose a branch-and-bound algorithm to efficiently search for optimal subgraph features by judiciously pruning the subgraph search space. Empirical studies on several real-world tasks demonstrate that our semi-supervised feature selection approach can effectively boost graph classification performances with semi-supervised feature selection and is very efficient by pruning the subgraph search space using both labeled and unlabeled graphs.
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