Proposal for a Statistical Reduct Method for Decision Tables
Proposal for a Statistical Reduct Method for Decision Tables
复制标题
决策表统计归约方法的提案
DOI：
10.1007/978-3-319-25754-9_13
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Shoutarou Mizuno
中科院分区：
文献类型：
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作者：
Y. Kato;T. Saeki;Shoutarou Mizuno
粗糙集理论被广泛用于估计和/或归纳决策表中约简后的IF-THEN规则的知识结构。约简的概念是通过充分必要条件属性来构造决策表来归纳规则。本文在对约简进行简要总结的基础上，利用模拟数据集对常规方法的约简进行了重新检验，并指出了其方法中存在的几个问题。在此基础上，提出了一种基于统计观点的约简方法。通过对仿真数据集和UCI数据集的应用，验证了该方法的有效性和实用性。特别是，本文给出了一种统计局部约简方法，对于估计隐藏在感兴趣的决策表后面的IF-THEN规则非常有用。
Rough Sets theory is widely used as a method for estimating and/or inducing the knowledge structure of if-then rules from a decision table after a reduct of the table. The concept of the reduct is that of constructing the decision table by necessary and sufficient condition attributes to induce the rules. This paper retests the reduct by the conventional methods by the use of simulation datasets after summarizing the reduct briefly and points out several problems of their methods. Then a new reduct method based on a statistical viewpoint is proposed. The validity and usefulness of the method is confirmed by applying it to the simulation datasets and a UCI dataset. Particularly, this paper shows a statistical local reduct method, very useful for estimating if-then rules hidden behind the decision table of interest.