Extended Belief Rule Base Reasoning Approach with Missing Data

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.