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Improving adaptive bagging methods for evolving data streams

  • Albert Bifet
  • , Geoff Holmes
  • , Bernhard Pfahringer
  • , Ricard Gavaldà
  • University of Waikato
  • Universidad Politecnica de Catalunia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

71 Citations (Scopus)

Abstract

We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.

Original languageEnglish
Title of host publicationAdvances in Machine Learning - First Asian Conference on Machine Learning, ACML 2009, Proceedings
Pages23-37
Number of pages15
DOIs
Publication statusPublished - 1 Dec 2009
Externally publishedYes
Event1st Asian Conference on Machine Learning, ACML 2009 - Nanjing, China
Duration: 2 Nov 2009 → 4 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Asian Conference on Machine Learning, ACML 2009
Country/TerritoryChina
CityNanjing
Period2/11/09 → 4/11/09

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