Graph classification: a diversified discriminative feature selection approach

Yuanyuan Zhu, J. Yu, Hong Cheng, Lu Qin
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引用次数: 40

Abstract

A graph models complex structural relationships among objects, and has been prevalently used in a wide range of applications. Building an automated graph classification model becomes very important for predicting unknown graphs or understanding complex structures between different classes. The graph classification framework being widely used consists of two steps, namely, feature selection and classification. The key issue is how to select important subgraph features from a graph database with a large number of graphs including positive graphs and negative graphs. Given the features selected, a generic classification approach can be used to build a classification model. In this paper, we focus on feature selection. We identify two main issues with the most widely used feature selection approach which is based on a discriminative score to select frequent subgraph features, and introduce a new diversified discriminative score to select features that have a higher diversity. We analyze the properties of the newly proposed diversified discriminative score, and conducted extensive performance studies to demonstrate that such a diversified discriminative score makes positive/negative graphs separable and leads to a higher classification accuracy.
图分类:一种多样化的判别特征选择方法
图对对象之间的复杂结构关系进行建模,并已广泛应用于各种应用中。构建自动图分类模型对于预测未知图或理解不同类之间的复杂结构变得非常重要。目前广泛使用的图分类框架包括两个步骤,即特征选择和分类。关键问题是如何从包含大量正图和负图的图数据库中选择重要的子图特征。给定所选的特征,可以使用通用分类方法来构建分类模型。在本文中,我们主要研究特征选择。我们发现了使用最广泛的特征选择方法的两个主要问题,即基于判别分数来选择频繁子图特征,以及引入新的多样化判别分数来选择具有更高多样性的特征。我们分析了新提出的多样化判别分数的性质,并进行了广泛的性能研究,证明了这种多样化判别分数使正/负图可分离,从而导致更高的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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