CAJ | 학술논문

在 AFS(axiomatic fuzzy set)理论框架下，提出了一种基于模糊概念相似性与模糊熵度量的分类算法。模糊分类规则的前件通过概念聚合得到，一种基于模糊概念相似性与模糊熵度量的概念选择函数指导聚合过程；然后，利用剪枝算法对得到的模糊规则集进行剪枝，得到最终的分类规则集。用8组来自 UCI数据库的数据集作为实验数据对算法进行验证，并与7种经典分类方法进行比较。实验结果表明该算法能得到较高的分类精度，分类结果明显优于参照的分类方法。
재 AFS(axiomatic fuzzy set)이론광가하，제출료일충기우모호개념상사성여모호적도량적분류산법。모호분류규칙적전건통과개념취합득도，일충기우모호개념상사성여모호적도량적개념선택함수지도취합과정；연후，이용전지산법대득도적모호규칙집진행전지，득도최종적분류규칙집。용8조래자 UCI수거고적수거집작위실험수거대산법진행험증，병여7충경전분류방법진행비교。실험결과표명해산법능득도교고적분류정도，분류결과명현우우삼조적분류방법。
A method to construct a fuzzy concept similarity and fuzzy entropy measure-based classifier by using the axiomatic fuzzy set (AFS)theory is developed.A selection index based on fuzzy concept similarity and fuzzy entropy measure is proposed.Being guided by the selection index,the antecedents of the fuzzy classification rules are selected from the fuzzy concepts which are found when using the aggregation algorithm.And then,the obtained fuzzy rules are pruned by pruning algorithm,and the final classification rule group is obtained.The performance of the proposed classifier is compared with the results produced by 7 classifiers commonly encountered in the literatures when using eight datasets taken from the UCI Machine Learning Repository.It has been found that the accuracy on test data produced by the proposed classifier is higher than that produced by the other classifiers.