Diversity in combinations of heterogeneous classifiers

Kuo Wei Hsu, Jaideep Srivastava

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

15 Scopus citations

Abstract

In this paper, we introduce the use of combinations of heterogeneous classifiers to achieve better diversity. Conducting theoretical and empirical analyses of the diversity of combinations of heterogeneous classifiers, we study the relationship between heterogeneity and diversity. On the one hand, the theoretical analysis serves as a foundation for employing heterogeneous classifiers in Multi-Classifier Systems or ensembles. On the other hand, experimental results provide empirical evidence. We consider synthetic as well as real data sets, utilize classification algorithms that are essentially different, and employ various popular diversity measures for evaluation. Two interesting observations will contribute to the future design of Multi-Classifier Systems and ensemble techniques. First, the diversity among heterogeneous classifiers is higher than that among homogeneous ones, and hence using heterogeneous classifiers to construct classifier combinations would increase the diversity. Second, the heterogeneity primarily results from different classification algorithms rather than the same algorithm with different parameters.

Original languageEnglish (US)
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages923-932
Number of pages10
DOIs
StatePublished - Jul 23 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: Apr 27 2009Apr 30 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/TerritoryThailand
CityBangkok
Period4/27/094/30/09

Keywords

  • Diversity
  • Ensemble
  • Heterogeneity
  • Multi-classifier system

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