Empirical evaluation of a new structure for AdaBoost

A. L.C. Barczak; M. J. Johnson; C. H. Messom

doi:10.1145/1363686.1364109

Empirical evaluation of a new structure for AdaBoost

A. L.C. Barczak, M. J. Johnson, C. H. Messom

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

5 Citations (Scopus)

Abstract

We propose a mixed structure to form cascades for AdaBoost classifiers, where parallel strong classifiers are trained for each layer. The structure allows for rapid training and guarantees high hit rates without changing the original threshold. We implemented and tested the approach for two datasets from UCI [1], and compared results of binary classifiers using three different structures: standard AdaBoost, a cascade classifier with threshold adjustments, and the proposed structure.

Original language	English
Title of host publication	SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	1764-1765
Number of pages	2
ISBN (Print)	9781595937537
DOIs	https://doi.org/10.1145/1363686.1364109
Publication status	Published - 2008
Externally published	Yes
Event	23rd Annual ACM Symposium on Applied Computing, SAC'08 - Fortaleza, Ceara, Brazil Duration: 16 Mar 2008 → 20 Mar 2008 Conference number: 23rd https://www.sigapp.org/sac/sac2008/

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Conference

Conference	23rd Annual ACM Symposium on Applied Computing, SAC'08
Abbreviated title	SAC
Country/Territory	Brazil
City	Fortaleza, Ceara
Period	16/03/08 → 20/03/08
Internet address	https://www.sigapp.org/sac/sac2008/

Access to Document

10.1145/1363686.1364109

Cite this

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title = "Empirical evaluation of a new structure for AdaBoost",

abstract = "We propose a mixed structure to form cascades for AdaBoost classifiers, where parallel strong classifiers are trained for each layer. The structure allows for rapid training and guarantees high hit rates without changing the original threshold. We implemented and tested the approach for two datasets from UCI [1], and compared results of binary classifiers using three different structures: standard AdaBoost, a cascade classifier with threshold adjustments, and the proposed structure.",

author = "Barczak, {A. L.C.} and Johnson, {M. J.} and Messom, {C. H.}",

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Barczak, ALC, Johnson, MJ & Messom, CH 2008, Empirical evaluation of a new structure for AdaBoost. in SAC '08: Proceedings of the 2008 ACM symposium on Applied computing. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery (ACM), New York, pp. 1764-1765, 23rd Annual ACM Symposium on Applied Computing, SAC'08, Fortaleza, Ceara, Brazil, 16/03/08. https://doi.org/10.1145/1363686.1364109

Empirical evaluation of a new structure for AdaBoost. / Barczak, A. L.C.; Johnson, M. J.; Messom, C. H.
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing. New York: Association for Computing Machinery (ACM), 2008. p. 1764-1765 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AB - We propose a mixed structure to form cascades for AdaBoost classifiers, where parallel strong classifiers are trained for each layer. The structure allows for rapid training and guarantees high hit rates without changing the original threshold. We implemented and tested the approach for two datasets from UCI [1], and compared results of binary classifiers using three different structures: standard AdaBoost, a cascade classifier with threshold adjustments, and the proposed structure.

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Y2 - 16 March 2008 through 20 March 2008

ER -

Empirical evaluation of a new structure for AdaBoost

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