基于粗糙集的多目标线性规划分类规则提取研究

Research on Rule Extraction from MCLP Classification Model Based on Rough Set

作为一种有效地分类方法,多目标线性规划已经被广泛应用到商业问题中。针对以数学模型表示的分类结果解释性差的问题,本文研究从线形规划分类模型中提取易于理解的分类规则的方法,打开McLP分类模型的＂黑箱＂。并利用粗糙集理论对MCLP分类模型不能区分的不确定区域进行表示和规则提取,提出了基于粗糙集的MCLP分类模型知识提取方法和算法。实验结果表明,该方法能够从分类结果中提取易于理解的规则并提高了MCLP的分类准确度。

As an effective model for classification,Multiple Criteria Linear Programming（MCLP）has been widely used in business intelligence.However,a possible limitation of MCLP is that it generates unexplainable black-box models which can only tell us results without reasons.To overcome this shortage,in this paper,we present a knowledge mining strategy based on rough set which mines explainable decision rules from black-box MCLP models.In the proposed approach,we use the rough set theory to express the uncertainty region.Finally,empirical studies on real world credit card data sets demonstrate that our method can effectively extract explicit rules from MCLP model and also improve the classification accuracy of MCLP.