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Conference Papers Year : 2012

Peer-to-peer multi-class boosting

Abstract

We focus on the problem of data mining over large-scale fully distributed databases, where each node stores only one data record. We assume that a data record is never allowed to leave the node it is stored at. Possible motivations for this assumption include privacy or a lack of a centralized infrastructure. To tackle this problem, earlier we proposed the generic gossip learning framework (GoLF), but so far we have studied only basic linear algorithms. In this paper we implement the well-known boosting technique in GoLF. Boosting techniques have attracted growing attention in machine learning due to their outstanding performance in many practical applications. Here, we present an implementation of a boosting algorithm that is based on FilterBoost. Our main algorithmic contribution is a derivation of a pure online multi-class version of FilterBoost, so that it can be employed in GoLF. We also propose improvements to GoLF, with the aim of maximizing the diversity of the evolving models gossiped in the network, a feature that we show to be important. We evaluate the robustness and the convergence speed of the algorithm empirically over three benchmark databases. We compare the algorithm with the sequential AdaBoost algorithm and we test its performance in a failure scenario involving message drop and delay, and node churn.
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Dates and versions

in2p3-00726735 , version 1 (31-08-2012)

Identifiers

Cite

István Hegedűs, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl. Peer-to-peer multi-class boosting. Euro-Par 2012 - 18th International European Conference on Parallel and Distributed Computing, Aug 2012, Rhodes Island, Greece. pp.389-400, ⟨10.1007/978-3-642-32820-6_39⟩. ⟨in2p3-00726735⟩
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