Lazy Bayesian Rules: A Lazy Semi-Naïve Bayesian Learning Technique Competitive to Boosting Decision Trees Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting.

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Presentation transcript:

Lazy Bayesian Rules: A Lazy Semi-Naïve Bayesian Learning Technique Competitive to Boosting Decision Trees Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting Deakin University Victoria Australia Appeared in ICML ‘99

Paper Overview Description of LBR, Adaboost and Bagging Experimental Comparison of algorithms

Naïve Bayesian Tree Each tree node is a naïve bayes classifier

Lazy Bayesian Rules Build a special purpose bayesian classifier based on the example to classify greedily choose which attributes to remain constant and which should vary

Boosting / Bagging Adaboost –train on examples –evaluate performance –re-train new classifier with weighted examples –repeat –when classifying, vote according to weights Bagging –train many times on samples drawn with replacement –when classifying, vote equally