Distributed classification in peer-to-peer networks

This work studies the problem of distributed classification in peer-to-peer (P2P) networks. While there has been a significant amount of work in distributed classification, most of existing algorithms are not designed for P2P networks. Indeed, as server-less and router-less systems, P2P networks impose several challenges for distributed classification: (1) it is not practical to have global synchronization in largescale P2P networks; (2) there are frequent topology changes caused by frequent failure and recovery of peers; and (3) there are frequent on-the-fly data updates on each peer. In this paper, we propose an ensemble paradigm for distributed classification in P2P networks. Under this paradigm, each peer builds its local classifiers on the local data and the results from all local classifiers are then combined by plurality voting. To build local classifiers, we adopt the learning algorithm of pasting bites to generate multiple local classifiers on each peer based on the local dat...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Ping Luo, Hui Xiong, Kevin Lü, Zhongzhi Shi
• Keywords: Data Mining, Dynamic P2p Networks, KDD 2007, Largescale P2p Networks, P2P Networks