Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment-猫眼课题宝

DOI：

10.52731/ijskm.v7.i1.692

发表时间：

2023

期刊：

International Journal of Service and Knowledge Management

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

H. Sakai;Zhiwen Jian

本文解决了从表格数据集生成规则的问题，并将获得的规则应用于决策支持。这里，考虑两种类型的表格数据集。其中一类是确定性信息系统(DIS)。另一类是用于处理不完全信息的非确定性信息系统。提出了两种新的规则生成算法：ReﬁNed和新实现的规则生成算法。所获得的每条规则都被用作决策的证据。因此，推理过程保持其透明度，这将是可解释人工智能的一个基本特征。由于所描述的一些改进，决策支持环境得到了加强，并且在Python中也得到了改进。在该网页上可以找到一些正在运行的Python视频。该框架几乎适用于任何表格数据集

This paper copes with rule generation from table data sets and applies the obtained rules to decision support. Here, two types of table data sets are considered. One type of them is speciﬁed as a Deterministic Information System (DIS). The other type is speciﬁed as a Non-deterministic Information System (NIS) for dealing with incomplete information. Two rule generation algorithms are reﬁned and newly implemented in Python. Every obtained rule is applied as evidence of decision-making. Therefore, the reasoning process preserves its transparency, which will be an essential characteristic for Explainable AI. The decision support environment is strengthened due to some described improvements and is also brushed up in Python. Some running videos of Python are available on the web page. This framework applies to almost any table data sets

Rule Generation from Several Types of Table Data Sets and Its Application: Decision-Making with Transparency and an Improved Execution Environment

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