Translated Title
Classification Approach Based on Improved Belief Rule-Base Reasoning
Translated Abstract
This paper proposes a new classification approach based on improved belief rule-base reasoning by intro-ducing linear combinational mode, setting the number of rules based on the classifications and improving the method of calculating individual matching degree. Compared with the traditional belief rule-base inference methodology, the number of rules in the proposed method does not depend on the number of antecedent attributes or its referential values, and it is only related to classification number. In this way, the new method can ensure the applicability for complex problems. In the experiments, the differential evolution algorithm is applied to train parameters, including rule weights, attribute weights, referential values of antecedent attributes and belief degrees. Three commonly public datasets have been employed to validate the proposed method. And the classification results are proved to be ideal, which shows that the proposed method is reasonable and effective.
Translated Keyword
belief rule-base
belief rule-base inference methodology using evidence reasoning (RIMER)
classification method
parameter learning
Access Number
WF:perioarticaljsjkxyts201605013
