论文标题
使用链接预测的多标签分类
Multi-Label Classification Using Link Prediction
论文作者
论文摘要
近年来，使用图形方法解决分类已广受欢迎。这是由于以下事实：数据可以直观地使用图形建模，以利用高级特征来帮助解决分类问题。使用链接预测进行分类的CULP是基于图的分类器。该分类器利用数据的图表表示，并将问题转换为链接预测的问题，我们试图在其中找到一个未标记的节点和适当的类节点之间的链接。 Culp被证明是高度准确的分类器，并且具有在几乎恒定时间内预测标签的能力。分类问题的一个变体是多标签分类，该分类可以解决此问题的多标签数据，其中一个实例可以具有与之关联的多个标签。在这项工作中，我们扩展了Culp算法以解决此问题。我们提出的扩展名传达了Culp的功能及其对多标签域的数据的直观表示，并且与某些最前沿的多标签分类器相比，产生了竞争性的结果。
Solving classification with graph methods has gained huge popularity in recent years. This is due to the fact that the data can be intuitively modeled with graphs to utilize high level features to aid in solving the classification problem. CULP which is short for Classification Using Link Prediction is a graph-based classifier. This classifier utilizes the graph representation of the data and transforms the problem to that of link prediction where we try to find the link between an unlabeled node and the proper class node for it. CULP proved to be highly accurate classifier and it has the power to predict the labels in near constant time. A variant of the classification problem is multi-label classification which tackles this problem for multi-label data where an instance can have multiple labels associated to it. In this work, we extend the CULP algorithm to address this problem. Our proposed extensions conveys the powers of CULP and its intuitive representation of the data in to the multi-label domain and in comparison to some of the cutting edge multi-label classifiers, yield competitive results.
