基于不完备信息系统的决策树生成算法

决策树是一种有效地进行实例分类的数据挖掘方法。在处理不完备信息系统中的缺省值数据时,现有决策树算法大多使用猜测技术。在不改变缺失值的情况下,利用极大相容块的概念定义了不完备决策表中条件属性对决策属性的决策支持度,将其作为属性选择的启发式信息。同时,提出了一种不完备信息系统中的决策树生成算法IDTBDS,该算法不仅可以快速得到规则集,而且具有较高的准确率。

Algorithm for Generating Decision Tree Based on Incomplete Information Systems

Decision trees are a kind of effective data mining methods to case classification. During processing objects with missing values in the incomplete information systems, the guessing technologies are often used in most of the existing decision tree algorithms. In this paper, we defined a condition attribute's decision support degree with respect to the decision attribute with the concept of a maximal consistent block, which can be regarded as the heuristic information.Moreover, we proposed an algorithm for generating a decision tree from an incomplete information system, which called IDTBDS. Note that the proposed algorithm not only fast extract the rule sets, and but also these rules possess more classification accuracy.