Title :
A Novel Classifier Selection Approach for Adaptive Boosting Algorithms
Author :
Ali, ABM Shawkat ; Dobele, Tony
Author_Institution :
Sch. of Comput. Sci., Central Queensland Univ., Rockhampton, QLD
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; AdaBoostMl algorithm; adaptive boosting algorithm; benchmark classification problem; machine learning; novel classifier selection approach; statistical information-based rule method; Australia; Boosting; Decision trees; Electronic mail; Error analysis; Machine learning; Machine learning algorithms; Performance evaluation; Testing; Training data;
Conference_Titel :
Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on
Conference_Location :
Melbourne, Qld.
Print_ISBN :
0-7695-2841-4
DOI :
10.1109/ICIS.2007.38
