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 proceedingConference contributionResearchpeer-review

5 Citations (Scopus)


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 languageEnglish
Title of host publicationSAC '08: Proceedings of the 2008 ACM symposium on Applied computing
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Print)9781595937537
Publication statusPublished - 2008
Externally publishedYes
Event23rd Annual ACM Symposium on Applied Computing, SAC'08 - Fortaleza, Ceara, Brazil
Duration: 16 Mar 200820 Mar 2008
Conference number: 23rd

Publication series

NameProceedings of the ACM Symposium on Applied Computing


Conference23rd Annual ACM Symposium on Applied Computing, SAC'08
Abbreviated titleSAC
CityFortaleza, Ceara
Internet address


Dive into the research topics of 'Empirical evaluation of a new structure for AdaBoost'. Together they form a unique fingerprint.

Cite this