So a new Bayesian model mixed tree augmented Naive Bayes classifier(MTANC) based on the rough set theory is presented.

因此，提出了一种基于粗糙集理论的混合树增广朴素贝叶斯分类模型（MTANC）。

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Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.

基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。

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Naive Bayes classification is a kind of simple and effective classification model. However, the performance of this model may be poor due to the assumption on the condition independence.

朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出现，致使其性能有所下降。

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