Semi-supervised multi-graph classification using optimal feature selection and extreme learning machine
Semi-supervised multi-graph classification using optimal feature selection and extreme learning machine
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DOI：
10.1016/j.neucom.2017.01.114
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发表时间：
2018-02
期刊：
影响因子：
6
通讯作者：
Jun Pang;Yu Gu;Jia Xu;Ge Yu
中科院分区：
文献类型：
--
作者：
Jun Pang;Yu Gu;Jia Xu;Ge Yu
多重图由一包图来表示。半监督多图分类是一种部分监督学习问题，在生物医药活性测试、科学出版物分类、在线产品推荐等领域有着广泛的应用。然而，据我们所知，很少有研究工作被报道。针对半监督多图分类问题，提出了一种半监督多图分类算法。我们的算法主要包括三个步骤，包括最优子图特征选择、多图的子图特征表示和半监督分类器的构建。我们首先提出了一种最优子图特征的评价准则，该准则不仅考虑了无标记多图，而且还考虑了多图层次和图层次之间的约束。然后，将最优子图特征选择问题等价地转化为挖掘信息量最大的子图特征问题。基于这些派生的子图特征，每个多重图由一个维向量表示，其中如果多个图中至少有一个图包含第i个子图特征，则其维度等于1。最后，在这些向量的基础上，采用半监督极值学习机(Semi-Supervised Elm，简称半监督ELM)建立了不可见多图标签的预测模型。在真实世界和合成图形数据集上的大量实验表明，该算法是有效和高效的。
A multi-graph is represented by a bag of graphs. Semi-supervised multi-graph classification is a partly supervised learning problem, which has a wide range of applications, such as bio-pharmaceutical activity tests, scientific publication categorization and online product recommendation. However, to the best of our knowledge, few research works have be reported. In this paper, we propose a semi-supervised multi-graph classification algorithm to handle the semi-supervised multi-graph classification problem. Our algorithm consists of three main steps, including the optimal subgraph feature selection, the subgraph feature representation of multi-graph and the semi-supervised classifier building. We first propose an evaluation criterion of the optimal subgraph features, which not only considers unlabeled multi-graphs but also considers the constraints between the multi-graph level and the graph level. Then, the optimal subgraph feature selection problem is equivalently converted into the problem of miningmmost informative subgraph features. Based on those derivedmsubgraph features, every multi-graph is represented by anm-dimensional vector, where theith dimension equals to 1 if at least one graph involved in the multi-graph contains theith subgraph feature. At last, based on these vectors, semi-supervised extreme learning machine(semi-supervised ELM) is adopted to build the prediction model for predicting the labels of unseen multi-graphs. Extensive experiments on real-world and synthetic graph datasets show that the proposed algorithm is effective and efficient.