Title :
Naive Bayes classifier: True and estimated errors for 2-class, 2-features case
Author_Institution :
Sch. of Informatics, Wales Univ., Bangor
Abstract :
The low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided
Keywords :
Bayes methods; errors; pattern classification; error rate estimation; feature dependency; naive Bayes classifier; Capacitive sensors; Cows; Diseases; Error analysis; Humans; Intelligent systems; Machine learning; Niobium; Pattern recognition; Probability; Machine Learning; NaYve Bayes; Pattern Recognition; error rates;
Conference_Titel :
Intelligent Systems, 2006 3rd International IEEE Conference on
Conference_Location :
London
Print_ISBN :
1-4244-01996-8
Electronic_ISBN :
1-4244-01996-8
DOI :
10.1109/IS.2006.348481
