Abstract
A multi-graph is represented by a bag of graphs and modelled as a generalization of a multi-instance. Multi-graph classification is a supervised learning problem for multi-graph, which has a wide range of applications, such as scientific publication categorization, bio-pharmaceu-tical 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.
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Notes
- 1.
DBLP dataset can be downloaded from http://arnetminer.org/citation.
- 2.
\(gMGFL+NBayes(or SVM, or ELM)\) denotes gMGFL using NBayes, SVM and ELM classification model, respectively. ME-\(MGC+PNBayes(ELM)\) represents ME-MGC using parallel NBayes and parallel ELM prediction model, respectively. In the case of without causing ambiguity, ME-MGC represents ME-\(MGC+ELM\).
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Acknowledgments
The work is partially supported by the National Basic Research Program of China (973 Program) (No. 2012CB316201), the National Natural Science Foundation of China (No. 61272179, No. 61472071).
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Pang, J., Gu, Y., Xu, J., Kong, X., Yu, G. (2016). Parallel Multi-graph Classification Using Extreme Learning Machine and MapReduce. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_7
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DOI: https://doi.org/10.1007/978-3-319-28397-5_7
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