Meta-classifiers and selective superiority

Ryan Benton, Miroslav Kubat, Rasaiah Loganantharaj

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

Abstract

Given that no one classification method is the best in all tasks, a variety of approaches have evolved to prevent poor performance due to mismatch of capabilities. One approach to overcome this problem is to determine when a method may be appropriate for a given problem. A second, more popular approach is to combine the capabilities of two or more classification methods. This paper provides some evidence that the combining of classifiers can yield more robust solutions.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages434-442
Number of pages9
Volume1821
ISBN (Print)3540676899, 9783540450498
DOIs
StatePublished - 2000
Externally publishedYes
Event13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000 - New Orleans, United States
Duration: Jun 19 2000Jun 22 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1821
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000
CountryUnited States
CityNew Orleans
Period6/19/006/22/00

Fingerprint

Classifiers
Classifier
Evidence

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Benton, R., Kubat, M., & Loganantharaj, R. (2000). Meta-classifiers and selective superiority. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1821, pp. 434-442). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1821). Springer Verlag. https://doi.org/10.1007/3-540-45049-1_53

Meta-classifiers and selective superiority. / Benton, Ryan; Kubat, Miroslav; Loganantharaj, Rasaiah.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1821 Springer Verlag, 2000. p. 434-442 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1821).

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

Benton, R, Kubat, M & Loganantharaj, R 2000, Meta-classifiers and selective superiority. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1821, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1821, Springer Verlag, pp. 434-442, 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2000, New Orleans, United States, 6/19/00. https://doi.org/10.1007/3-540-45049-1_53
Benton R, Kubat M, Loganantharaj R. Meta-classifiers and selective superiority. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1821. Springer Verlag. 2000. p. 434-442. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/3-540-45049-1_53
Benton, Ryan ; Kubat, Miroslav ; Loganantharaj, Rasaiah. / Meta-classifiers and selective superiority. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1821 Springer Verlag, 2000. pp. 434-442 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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