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A novel classifier selection approach for adaptive boosting algorithms

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conference contribution
posted on 06.12.2017, 00:00 by A B M Shawkat Ali, Anthony Dobele
Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostM1. A trial and error classifier feeding with the AdaBoostM1 algorithm is a regular practice for classification tasks in the research community. We provide a novel statistical information-based rule method for unique classifier selection with the AdaBoostM1 algorithm. The solution also verified a wide range of benchmark classification problems.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

532

End Page

536

Number of Pages

5

Start Date

11/07/2007

Finish Date

13/07/2007

ISBN-10

0-7695-2841-4

ISBN-13

9780769528410

Location

Melbourne, Australia

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

IEEE International Conference on Computer and Information Science

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