Compact dual ensembles for active learning

Amit Mandvikar, Huan Liu, Hiroshi Motoda

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

2 Scopus citations


Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use diversity of the member classifiers of an ensemble for efficient active learning. We empirically show, using benchmark data sets, that (1) to achieve a good (stable) ensemble, the number of classifiers needed in the ensemble varies for different data sets; (2) feature selection can be applied for classifier selection from ensembles to construct compact ensembles with high performance. Benchmark data sets and a real-world application are used to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
EditorsHonghua Dai, Ramakrishnan Srikant, Chengqi Zhang
PublisherSpringer Verlag
Number of pages5
ISBN (Print)354022064X, 9783540220640
StatePublished - 2004
Event8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: May 26 2004May 28 2004

Publication series

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


Conference8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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