基于置信规则库推理的二择众仓分类方法

针对线性组合方式所构建的置信规则库存在常常无法准确发挥前件属性权重的效能，且随着评价等级个数的增加，新激活权重公式往往会对结果造成不利影响的不足，本文在现有置信规则库推理分类算法的基础上，提出二择众仓决策法，以此改进置信规则库决策系统。首先仅设置两个规则的后件评价等级，对一个决策问题仅做出二择判定，即回答是与否；其次，设置多个置信规则库同时处理若干个子问题；最后通过众仓决策方式融合多个子问题的结果，进而解决最终的分类问题。实验结果表明，改进后的置信规则库推理分类方法可行有效。

Two-Value Judgment Classification Approach Based on Belief Rule-Base Reasoning

The weight of antecedent attributes can''t work accurately in the linear combinational belief rule based system usually. Simultaneously, with an increase in the number of evaluation ranks, the new weight activation formula will have negative effects on results. Aiming at the above drawbacks, this paper proposes a two-value and multi-base reasoning method based on the existing belief rule based inference classification algorithm to improve the belief rule based decision system. The evaluation of belief rules in the conclusion are divided into two ranks firstly, which means making a two-value judgment on a decision problem. Then many belief rule bases are set to solve some sub problems simultaneously. Finally results of many sub problems by multi-base reasoning method are mixed to solve the classification problem. Experimental results show the feasibility and effectiveness of the proposed belief rule base reasoning classfication method.