A Novel Classifier Selection Approach for Adaptive Boosting Algorithms

A Novel Classifier Selection Approach for Adaptive Boosting Algorithms

Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostMl. A trial and error classifier feeding with the AdaBoostMl algorithm is a regular practice for classification tasks in the research communi...

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Bibliographische Detailangaben
Hauptverfasser: Ali, S., Dobele, T.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Australia
Boosting
Decision trees
Electronic mail
Error analysis
Machine learning
Machine learning algorithms
Performance evaluation
Testing
Training data
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Beschreibung
Zusammenfassung:Boosting is a general approach for improving classifier performances. In this research we investigated these issues with the latest Boosting algorithm AdaBoostMl. A trial and error classifier feeding with the AdaBoostMl 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 AdaBoostMl algorithm. The solution also verified a wide range of benchmark classification problems.
DOI:10.1109/ICIS.2007.38