A novel classifier selection approach for adaptive boosting algorithms
conference contributionposted on 2017-12-06, 00:00 authored by A B M Shawkat Ali, Anthony DobeleAnthony 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.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages5
Place of PublicationUSA
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External Author AffiliationsFaculty of Business and Informatics;