Extended Belief Rule Base Reasoning Approach with Missing Data

被引：0
|
作者：
Liu Y. [1 ]
Gong X. [2 ]
Fang W. [1 ]
Fu Y. [1 ]
机构：
[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou
[2] Institute of Decision Sciences, Fuzhou University, Fuzhou
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2022年 / 59卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Belief rule base; Data driven; Data missing; Evidence reasoning; Incomplete dataset;
D O I：
10.7544/issn1000-1239.20200702
中图分类号：
学科分类号：
摘要：
The data-driven constructed extended belief rule-based system can deal with uncertainty problems with both quantitative data and qualitative knowledge. It has been widely researched and applied in recent years, but infrequently been involved in the field of incomplete data. This study conducts research focusing on the performance of the extended belief rule-based system applied to incomplete datasets and proposes a novel reasoning approach for the case of data missing. First, a disjunctive extended rule base is constructed and the optimal number of antecedent attribute referential values is discussed through validation experiments. Then a method for generating a disjunctive belief rule base from incomplete data and consisting of disjunctive belief rule base is proposed, and an attenuation factor is introduced to modify the weight of incomplete rules to make the aggregation of information more reasonable. Finally, this paper conducts experiments on several commonly used datasets selected from UCI to validate the improvement of the proposed method. The experiments are designed with various degrees and patterns of data missing, and the performance of the improved system is analyzed and compared with some conventional mechanisms. Experimental comparison with other methods shows that while the new method performs well on complete datasets, it also shows better and more stable inference effects on datasets with different degrees of missing and patterns. © 2022, Science Press. All right reserved.
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页码：661 / 673
页数：12
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