Parallel multi-graph classification using extreme learning machine and MapReduce
Parallel multi-graph classification using extreme learning machine and MapReduce
复制标题
使用极限学习机和 MapReduce 的并行多图分类
DOI：
10.1016/j.neucom.2016.03.111
复制
发表时间：
2017
期刊：
影响因子：
6
通讯作者：
Ge Yu
中科院分区：
文献类型：
--
作者：
Jun Pang;Yu Gu;Jia Xu;Xiaowang Kong;Ge Yu
A multi-graph is represented by a bag of graphs and modeled as a generalization of a multi-instance. Multi-graph classification is a supervised learning problem, which has a wide range of applications, such as scientific publication categorization, bio-pharmaceutical activity tests and online product recommendation. However, existing algorithms are limited to process small datasets due to high computation complexity of multi-graph classification. Specially, the precision is not high enough for a large dataset. In this paper, we propose a scalable and high-precision parallel algorithm to handle the multi-graph classification problem on massive datasets using MapReduce and extreme learning machine. Extensive experiments on real-world and synthetic graph datasets show that the proposed algorithm is effective and efficient.
