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- Title
不完备信息条件下基于 Roustida 改进算法的 诊断规则提取.
- Authors
李金艳; 余忠华
- Abstract
The decision-making process is essentially the information processing. The complexity of the information structure and the limitations of data collection usually lead to incomplete phenomena, such as missing, fuzzy and redundant information, which affects the accuracy of diagnosis. For this reason, discussion on how to improve the completeness of the diagnosed information was conducted, and knowledge mining methods of information completion and attribute reduction were proposed. Firstly, the limitations of Roustida algorithm in processing missing values were improved, so as to expand the scope of its application in complex engineering practice and to make the missing information complete. Then, the conditional attribute reduction and rule extraction were carried out by using the genetic algorithm and the generalized diagnostic rule reasoning respectively. Finally, a case study was carried out on the diagnosis of quality issues. It is verified that this method can be used to express the correlation between the problem and contextual information in a relatively simple way under the condition of incomplete information, and explore the underlying laws of problem occurrence.
- Publication
Science Technology & Engineering, 2023, Vol 23, Issue 35, p15117
- ISSN
1671-1815
- Publication type
Article