On the performance of stacked generalization classifiers

Mete Ozay, Fatos Tunay Yarman Vural

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

12 Citations (Scopus)

Abstract

Stacked Generalization (SG) is an ensemble learning technique, which aims to increase the performance of individual classifiers by combining them under a hierarchical architecture. In many applications, this technique performs better than the individual classifiers. However, in some applications, the performance of the technique goes astray, for the reasons that are not well-known. In this work, the performance of Stacked Generalization technique is analyzed with respect to the performance of the individual classifiers under the architecture. This work shows that the success of the SG highly depends on how the individual classifiers share to learn the training set, rather than the performance of the individual classifiers. The experiments explore the learning mechanisms of SG to achieve the high performance. The relationship between the performance of the individual classifiers and that of SG is also investigated.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 5th International Conference, ICIAR 2008, Proceedings
Pages445-454
Number of pages10
DOIs
Publication statusPublished - 2008 Jul 28
Event5th International Conference on Image Analysis and Recognition, ICIAR 2008 - Povoa de Varzim, Portugal
Duration: 2008 Jun 252008 Jun 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5112 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Image Analysis and Recognition, ICIAR 2008
CountryPortugal
CityPovoa de Varzim
Period08/6/2508/6/27

Keywords

  • Ensemble learning
  • Parallel computing
  • Pattern recognition
  • Stacked generalization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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