论文标题
网络中的层次多标签分类的自上而下的监督学习方法
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
论文作者
论文摘要
节点分类是从网络中其他节点可用的信息中推断或预测缺失节点属性的任务。本文介绍了分层多标签分类（HMC）的一般预测模型，其中要推断的属性可以指定为严格的POSET。它基于一种自上而下的分类方法，该方法通过每班构建本地分类器来解决层次多标签分类，并通过监督学习。提出的模型通过案例研究展示了有关多种大米的Oryza sativa japonica的基因功能的预测。它可以与分层二项式 - 纽伯格（一种概率模型）通过评估这两种方法在预测性能和计算成本方面进行了比较。这项工作的结果支持了一个工作假设，即所提出的模型可以达到良好的预测效率，同时扩大了与艺术的状态相关的。
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network. This paper presents a general prediction model to hierarchical multi-label classification (HMC), where the attributes to be inferred can be specified as a strict poset. It is based on a top-down classification approach that addresses hierarchical multi-label classification with supervised learning by building a local classifier per class. The proposed model is showcased with a case study on the prediction of gene functions for Oryza sativa Japonica, a variety of rice. It is compared to the Hierarchical Binomial-Neighborhood, a probabilistic model, by evaluating both approaches in terms of prediction performance and computational cost. The results in this work support the working hypothesis that the proposed model can achieve good levels of prediction efficiency, while scaling up in relation to the state of the art.
