基于改进变精度贝叶斯粗糙集的感性知识获取

Kansei Knowledge Acquisition Based on the Improved Variable Precision Bayesian Rough Set

变精度贝叶斯粗糙集方法是感性知识获取中处理用户群个性化感性差异的一种柔性方法,针对其在感性规则提取阶段可能产生的组合爆炸问题,提出了一种基于顺序覆盖策略的改进算法.该算法以感性决策类的近似区域作为输入,以选取覆盖能力最大的合取项为贪心搜索策略实现规则特化.在此基础上,通过迭代学习逐步完成对近似区域的覆盖和决策规则集的提取.最后,通过基础实例和烤面包机外观设计实例验证了改进方法的有效性.

The variable precision Bayesian rough set(VPBRS)approach is a flexible method for Kansei knowledge acquisition to accommodate the individual differences within a user group.In order to handle the possible combinatorial explosion at the stage of Kansei rule extraction,an improved algorithm based on sequential covering strategy is proposed.Basically,the approximation regions of Kansei decision classes are taken as the input,and the selection of conjunctive items with maximum covering ability is taken as the greedy search strategy for rule specialization.On this basis,the approximation region is covered step by step through iterative learning,and the decision rule set is extracted.A basic example and a design example of toaster appearance are conducted,whose results show that the improved VPBRS approach is effective.