Cascading customized naïve bayes couple

Authors:
Guichong Li;Nathalie Japkowicz;Trevor J. Stocki;R. Kurt Ungar
Affiliations:
Computer Science, University of Ottawa, Ottawa, Canada;Computer Science, University of Ottawa, Ottawa, Canada;Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada;Radiation Protection Bureau, Health Canada, Ottawa, ON, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

Naïve Bayes (NB) is an efficient and effective classifier in many cases However, NB might suffer from poor performance when its conditional independence assumption is violated While most recent research focuses on improving NB by alleviating the conditional independence assumption, we propose a new Meta learning technique to scale up NB by assuming an altered strategy to the traditional Cascade Learning (CL) The new Meta learning technique is more effective than the traditional CL and other Meta learning techniques such as Bagging and Boosting techniques while maintaining the efficiency of Naïve Bayes learning.