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-\(MGC+ELM\).
References
Wu, J., Zhu, X., Zhang, C., et al.: Bag constrained structure pattern mining for multi-graph classification. TKDE 26(10), 2382–2396 (2014)
Wu, J., Pan, S., Zhu, X., et al.: Boosting for multi-graph classification. T. Cybern. 45(3), 430–443 (2015)
MapReduce. http://en.wikipedia.org/wiki/MapReduce
Huang, G., Zhu, Q., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: IJCNN, pp. 985–990 (2004)
Huang, G., Liang, N., Rong, H., et al.: On-line sequential extreme learning machine. In: IASTED, pp. 232–237 (2005)
He, Q., Shang, T., Zhuang, F., et al.: Parallel extreme learning manchine for regression based on MapReduce. Neurocomputing 102(2), 52–58 (2013)
Xin, J., Wang, Z., Chen, C., et al.: \(ELM^*\): distributed extreme learning machine with MapReduce. World Wide Web 17(5), 1189–1204 (2014)
Xin, J., Wang, Z., Qu, L., et al.: Elastic extreme learning machine for big data classification. Neurocomputing, 149(Part A), 464–471 (2015)
Bi, X., Zhao, X., Wang, G., et al.: Distributed extreme learning machine with kernels based on MapReduce. Neurocomputing 149, 456–463 (2015)
Wang, B., Huang, S., Qiu, J., et al.: Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149, 224–232 (2015)
Kuramochi, M., Karypis, G.: Finding frequent patterns in a large sparse graph. In: SDM, pp. 345–356 (2004)
Kuramochi, M., Karypis, G.: Grew-a-scalable frequent subgraph discovery algorithm. In: ICDM, pp. 439–442 (2004)
Hill, S., Srichandan, B., Sunderraman, R.: An Iterative mapreduce approach to frequent subgraph mining in biological datasets. In: BCB, pp. 661–666 (2012)
Lin, W., Xiao, X., Ghinita, G.: Large-scale frequent subgraph mining in MapReduce. In: ICDE, pp. 844–855 (2014)
Huang, G., Zhu, Q., Siew, C.K., et al.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Huang, G., Chen, L.: Enhanced random search based incremental extreme learning machine. Neurocomputing 71(16–18), 3460–3468 (2008)
Huang, G., Ding, X., Zhou, H.: Optimization method based extreme learning machine for classification. Neurocomputing 74(1–3), 155–163 (2010)
Huang, G., Wang, D., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. Cybern 2(2), 107–122 (2011)
Huang, G., Zhou, H., Ding, X., et al.: Extreme learning machine for regression and multiclass classification. In: TSMC. Part B Cybern. 42(2), 513–529 (2012)
Huang, G., Wang, D.: Advances in extreme learning machines (ELM2011). Neurocomputing 102, 1–2 (2013)
Huang, G.: An insight into extreme learning machines: random neurons, random features and kernels. Cogn. Comput. 6(3), 376–390 (2014)
Huang, G., Bai, X., Kasun, L.L.C., et al.: Local receptive fields based extreme learning machine. In: Comp. Int. Mag. 10(2), 18–29 (2015)
Wang, G., Zhao, Y., Wang, D.: A protein secondary structure prediction framework based on the extreme learning machine. Neurocomputing 72(1–3), 262–268 (2008)
Zhao, X., Wang, G., Bin, X., et al.: XML document classification based on ELM. Neurocomputing 74(16), 2444–2451 (2011)
Sun, Y., Yuan, Y., Wang, G.: An OS-ELM based distributed ensemble classification framework in P2P networks. Neurocomputing 74(16), 2438–2443 (2011)
Wang, Z., Zhao, Y., Wang, G., et al.: On extending extreme learning machine to non-redundant synergy pattern based graph classification. Neurocomputing 149, 330–339 (2015)
Perusich, K., Senior, M.: Using fuzzy connitive maps for knowledge management in a conflict environment. TSMC. Part C 36(6), 810–821 (2006)
Pubchem. http://pubchem.ncbi.nlm.nih.gov
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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