The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.

基于朴素贝叶斯分类方法的实验表明，提出的方法能够有效提高中文文本的分类准确率。

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This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction.

围绕着分类挖掘中的隐私保护问题展开研究，给出了一种基于数据处理和特征重构的朴素贝叶斯分类中的隐私保护方法。

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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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