Lazy Learning Based Double Layer Naive Bayesian Classifier
L^2DLNB：懒惰学习双层朴素贝叶斯分类器

Though nave Bayesian classifier is simple and has good performance on many data sets, its attribute independence assumption does not always exist in the real world. Its performance is poor while the assumption is violated. In order to relax this assumption, L2DLNB( Lazy Learning Based Double Layer Nave Bayesian classifier ), is proposed, which could accurately calculate the likelihood, using condition mutual information based lazy learning, and different attribute dependent relation when to calculate the likelihood of different label. Experimt results indicate that L2DLNB improves classifier accuracy on some datasets compared to other Bayesian classifiers.