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| 1The naive Bayesian classifier is a simple and effective approach to classifier learning.
However, its conditionally independent assumption doesn’t often hold in the real
world and this will lead to accuracy decrease in some applications. LBR, TAN and
AODE are several representative algorithms that seek to relax this assumption and
have exhibited noticeable prediction performance. However, the computational cost
of LBR and TAN is considerable. AODE is creditably an effective classification
learning algorithm without increasing computational cost improperly. However, to
intently debase computational complexity, AODE avoids model selection and adopts
all SPODEs , this may result in insufficiency to improve the prediction accuracy
because of the included SPODEs which bring negative effect. Therefore, we propose
an improved algorithm in this paper which will improve classification accuracy and
classification speed by filtering out those SPODEs which bring negative effect base
on original algorithms. |
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| Keywords:Naive Bayes, Classifier, Cross validation |
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