期刊
NEUROCOMPUTING
卷 74, 期 16, 页码 2438-2443出版社
ELSEVIER
DOI: 10.1016/j.neucom.2010.12.040
关键词
Peer-to-Peer (P2P); Extreme learning machine; OS-ELM; Ensemble classification; Parallel ensemble classification; Quad-tree
资金
- National Natural Science Foundation of China [60873011, 60933001, 61025007]
- National Basic Research Program of China [2011CB302200-G]
- 863 High Technology Program [2009AA01Z150]
- Fundamental Research Funds for the Central Universities [N090104001, N090304007]
Although classification in centralized environments has been widely studied in recent years, it is still an important research problem for classification in P2P networks due to the popularity of P2P computing environments. The main target of classification in P2P networks is how to efficiently decrease prediction error with small network overhead. In this paper, we propose an OS-ELM based ensemble classification framework for distributed classification in a hierarchical P2P network. In the framework, we apply the incremental learning principle of OS-ELM to the hierarchical P2P network to generate an ensemble classifier. There are two kinds of implementation methods of the ensemble classifier in the P2P network, one-by-one ensemble classification and parallel ensemble classification. Furthermore, we propose a data space coverage based peer selection approach to reduce high the communication cost and large delay. We also design a two-layer index structure to efficiently support peer selection. A peer creates a local Quad-tree to index its local data and a super-peer creates a global Quad-tree to summarize its local indexes. Extensive experimental studies verify the efficiency and effectiveness of the proposed algorithms. (C) 2011 Elsevier B.V. All rights reserved.
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