非一致决策表的决策树分析

Decision Tree Analysis for Inconsistent Decision Tables

摘要: 决策树技术在数据挖掘的分类领域中被广泛采用。采用决策树从一致决策表(即条件属性值相同的样本其决策值相同)中挖掘有价值信息的相关研究较为成熟，而对于非一致决策表(即条件属性值相同的样本其决策值不同)采用决策树进行数据挖掘是当前研究热点。本文基于贪心算法的思想，提出了一种非一致决策表的决策树分析方法。首先使用多值决策方法处理非一致决策表，将非一致决策表转换成多值决策表(即用一个集合表示样本的多个决策值)；然后根据贪心选择思想，使用不纯度函数和不确定性相关指标设计贪心选择策略；最后使用贪心选择设计决策树构造算法实现决策树构造。通过实例说明了所提出的权值和贪心选择指标能够比已有的最大权值贪心选择指标生成规模更小的决策树。

Abstract: Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. Based on the greedy algorithm, we propose a new approach to construct a decision tree for inconsistent decision table. Firstly, an inconsistent decision table is transformed into a many-valued decision table. After that, we develop a greedy algorithm using “weighted sum” as the impurity and uncertainty measure to construct a decision tree for inconsistent decision tables. An illustration example is used to show that our “weighted sum” measure is better than the existing “weighted max” measure to reduce the size of constructed decision tree.