Communication-Efficient Classification in P2P Networks

Autor:	Vivekanand Gopalkrishnan, Hock Hee Ang, Wee Keong Ng, Steven C. H. Hoi
Rok vydání:	2009
Předmět:	Exploit Computer science business.industry Distributed computing Bandwidth (signal processing) Machine learning computer.software_genre Class (biology) Support vector machine Physical address High availability Scalability Artificial intelligence Dynamism business computer
Zdroj:	Machine Learning and Knowledge Discovery in Databases ISBN: 9783642041792 ECML/PKDD (1)
DOI:	10.1007/978-3-642-04180-8_23
Popis:	Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR--a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust to parameters such as the number of peers in the network, imbalanced data sizes and class distribution while incurring extremely low communication cost yet maintaining accuracy comparable to the best-in-the-class approaches. Feasibility and effectiveness of our approach are demonstrated with extensive experimental studies on real and synthetic datasets.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8d8803d4cb2622f7c4a158961a1f235f https://doi.org/10.1007/978-3-642-04180-8_23 Zobrazit plný text záznamu