Decision Tree Analysis for Inconsistent Decision Tables
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.
文章引用: 许美玲 , 乔莹 , 曾静 , 莫毓昌 , 钟发荣 (2016) 非一致决策表的决策树分析。 计算机科学与应用， 6， 597-606. doi: 10.12677/CSA.2016.610074
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