Peer-to-peer multi-class boosting

István Hegedus, Róbert Busa-Fekete, Róbert Ormándi, Márk Jelasity, Balázs Kégl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationParallel Processing - 18th International Conference, Euro-Par 2012, Proceedings
Pages389-400
Number of pages12
DOIs
Publication statusPublished - Oct 24 2012
Event18th International Conference on Parallel Processing, Euro-Par 2012 - Rhodes Island, Greece
Duration: Aug 27 2012Aug 31 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7484 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Parallel Processing, Euro-Par 2012
CountryGreece
CityRhodes Island
Period8/27/128/31/12

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Keywords

  • FilterBoost
  • P2P
  • boosting
  • gossip
  • multi-class classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hegedus, I., Busa-Fekete, R., Ormándi, R., Jelasity, M., & Kégl, B. (2012). Peer-to-peer multi-class boosting. In Parallel Processing - 18th International Conference, Euro-Par 2012, Proceedings (pp. 389-400). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7484 LNCS). https://doi.org/10.1007/978-3-642-32820-6_39