一种多属性和准则定序分类模型

Sequencing classification model on multiple attributes and criterias

针对经典粗糙集理论在解决定性属性、定量属性以及准则属性同时出现的定序分类问题时能力较弱的这种情况,对经典粗糙集理论进行扩展,并给出了一个基于扩展粗糙集的决策分析方法.该方法使用"不可区分-相似-优势"关系来代替经典粗糙集理论中的不可区分关系来获取知识的粗糙近似.实例验证表明该方法不但能够解决上述问题而且还能处理决策表中可能存在的不一致现象,具有较好的有效性与优越性.

Classic rough set theory solves problems by means of indiscernibility relation, but it is powerless to resolve the sequencing classification problems in which qualitative attributes, quantitative attributes and criteria exist at the same time. In view of this situation, classic rough set theory is extended, and then a decision analysis method based on the extended rough set theory is proposed. This method replaces the indiscernibility relation in original rough set theory with the indiscernibility-similarity-dominance relation and achieves rough approximation of knowledge. An example study shows that the method can effecively resolve the above mentioned problems and can deal with inconsistence in decision table.