Error-sensitive grading for model combination

Surendra K. Singhi, Huan Liu

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

Abstract

Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of classifiers is to decide upon a way of combining the prediction of its base classifiers. In this paper, we introduce a novel grading-based algorithm for model combination, which uses cost-sensitive learning in building a meta-learner. This method distinguishes between the grading error of classifying an incorrect prediction as correct, and the other-way-round, and tries to assign appropriate costs to the two types of error in order to improve performance. We study issues in error-sensitive grading, and then with extensive experiments show the empirically effectiveness of this new method in comparison with representative meta-classification techniques.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages724-732
Number of pages9
DOIs
StatePublished - Dec 1 2005
Event16th European Conference on Machine Learning, ECML 2005 - Porto, Portugal
Duration: Oct 3 2005Oct 7 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3720 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th European Conference on Machine Learning, ECML 2005
CountryPortugal
CityPorto
Period10/3/0510/7/05

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Singhi, S. K., & Liu, H. (2005). Error-sensitive grading for model combination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 724-732). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3720 LNAI). https://doi.org/10.1007/11564096_74