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中文摘要：
提出一种基于连续属性离散化的知识分类方法.将条件属性按照重要度由高到低排序,并依照此排序将决策表中各条件属性依次离散化.在对决策表中条件属性的离散化过程中充分考虑已离散化的条件属性及决策属性,离散后的决策表不需要进一步约简.使用了模拟数据和UCI机器学习数据集中的数据进行算法测试,而且与其他离散化算法进行了对比,结果充分证明了新方法的有效性.
英文摘要：
This paper gives a new method of classification based on discretization of continuous attributes.Firstly condition attributes are sorted in descending order by their significance,and then each condition attribute in the decision table is discretized in sequence by the order.Both discretized condition attributes and decision attributes are paid more attention during the course of discretization.And the discretized decision table needs not to be reduced further.Finally,the simulation data and the UCI machine learning data are used to verify the new method,and the new method is compared with other discretization algorithms.The results fully show the correctness and effectiveness of the proposed method of classification based on discretization of continuous attributes.
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