Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.03.111 | Neurocomputing |
Keywords | Field | DocType |
Multi-graph,Classification,Extreme learning machine,MapReduce | Data mining,One-class classification,Graph classification,Extreme learning machine,Computer science,Artificial intelligence,Categorization,Pattern recognition,Parallel algorithm,Supervised learning,Machine learning,Computation complexity,Scalability | Journal |
Volume | ISSN | Citations |
261 | 0925-2312 | 3 |
PageRank | References | Authors |
0.37 | 27 | 5 |