摘要
在现实决策中,代价敏感问题是影响人类决策的重要因素之一,许多研究者致力于降低决策的代价。现阶段,在粗糙集领域中,研究者多基于DTRS模型且仅考虑某一种代价,不够全面。针对以上问题,利用序贯三支决策模型对两种代价的敏感性,通过多层次粒结构可以有效降低决策总代价,且能够更好地模拟人类动态渐进的决策过程。在序贯三支决策模型的基础上,构造了多层次粒结构;将各个属性的测试代价与其分类能力相关联,从信息熵的角度为其设置测试代价;与此同时,将属性约简与序贯三支决策相结合,利用基于代价最小准则的属性约简去除冗余属性及不相关属性对代价的影响。在7个UCI数据集上的实验结果显示,在保证较高准确度的同时,决策的总代价平均下降了26%左右,充分验证了该方法的有效性。
In realistic decision-making,cost-sensitive issue is one of the important factors which affects human decision-making,and many researchers are committed to reducing the cost of decision-making.At present,in the field of rough set,many researchers mainly research decision-making based on DTRS model and only consider a certain cost,which is not comprehensive enough.While sequential thre e-way decision model is sensitive to two kinds of costs,and the multi-level granular structure can effectively reduce the total cost of decision and can better simulate the process of human’s dynamic and gradual decision-making.Based on sequential three decision models,this paper constructed a multi-level granular structure.It relates the test cost of each attribute to its classification ability and sets the test cost from the perspective of information entropy.At the same time,combined with the sequential three decisions,the attribute reduction based on the minimum cost criterion is used to remove the influence of redundant attributes and irrelevant attributes on the cost.The experimental results on the seven UCI datasets show that while high accuracy is ensured,the total cost of decision-making is dropped by an average of 26%,which fully validates the effectiveness of the proposed method.
作者
邢颖
李德玉
王素格
XING Ying;LI De-yu;WANG Su-ge(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University,Taiyuan 030006,China)
出处
《计算机科学》
CSCD
北大核心
2018年第10期6-10,共5页
Computer Science
基金
国家自然科学基金资助项目(61672331
61632011
61573231
61432011
61603229)
山西省自然科学基金项目(201601D021076)资助
