Abstract: Discretization is an effective technique to deal with continuous attributes for machine learning and data mining.Reasonability of a discretization process determines the accuracy of expression and extraction for information.Dealing with the discretization of real value attributes，Chi2 algorithm can get a good result of the conflict-free data but do not well in inconsistency and incomplete data.This paper makes full use of the Bayseian model which allows for the wrong classification in nature and improved the Chi2 algorithm.The improved algorithm is not only more suitable for inconsistency and incomplete data，but also make the interval merging more reasonable.The experimental results have proven the validity of the new algorithm.

摘要： 连续属性离散化在机器学习和数据挖掘领域中有着重要的作用。连续属性离散化方法是否合理决定着对信息的表达和提取的准确性。Chi2算法在对连续属性进行离散化处理时，无冲突的数据能够得到较好的结果，但是，对不协调和不完全的数据实验结果不是很理想。利用了Bayseian模型允许一定程度错误分类存在的性质，对Chi2算法进行了改进。改进后的Chi2算法不仅更适合不协调和不完全的数据，还使得区间的合并更加合理。实验结果证明了算法的有效性。