[1]杨洁,王国胤,张清华.基于知识距离的粗糙粒结构的评价模型[J].智能系统学报,2020,15(1):166-174.[doi:10.11992/tis.201904037]
YANG Jie,WANG Guoyin,ZHANG Qinghua.Evaluation model of rough granular structure based on knowledge distance[J].CAAI Transactions on Intelligent Systems,2020,15(1):166-174.[doi:10.11992/tis.201904037]
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YANG Jie,WANG Guoyin,ZHANG Qinghua.Evaluation model of rough granular structure based on knowledge distance[J].CAAI Transactions on Intelligent Systems,2020,15(1):166-174.[doi:10.11992/tis.201904037]
基于知识距离的粗糙粒结构的评价模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第1期
页码:
166-174
栏目:
人工智能院长论坛
出版日期:
2020-01-05
- Title:
- Evaluation model of rough granular structure based on knowledge distance
- Keywords:
- rough granular structure; knowledge distance; uncertainty measure; evaluation model; granular computing; rough sets; constraint condition; uncertainty measure
- 分类号:
- TP311
- 摘要:
- 在粒计算理论中，通过不同的粒计算机制可以生成不同的粒结构。在粗糙集中，对于同一个信息表而言，通过不同的属性添加顺序可以得到由不同的序贯层次结构，即粗糙粒结构。在粗糙粒结构中，不同的属性获取顺序导致了对不确定性问题求解的不同程度。因此，如何有效评价粗糙粒结构是一个值得研究的问题。本文将从知识距离的角度研究这个问题。首先，在前期工作所提出的知识距离框架上提出了一种粗糙近似空间距离，用于度量粗糙近似空间之间差异性。基于提出的知识距离，研究了粗糙粒结构的结构特征。在粗糙粒结构中，在对不确定性问题进行求解时，本文希望在约束条件下可以利用尽可能少的知识空间使不确定性降低达到最大化。基于这个思想并利用以上得出的结论，在属性代价约束条件下，引入了一个评价参数λ，并在此基础建立了一种粗糙粒结构的评价模型，该方法实现了在属性代价约束条件下选择粗糙粒结构的功能。最后，通过实例验证了本文提出的模型的有效性。
- Abstract:
- In the theory of granular computing (GrC), different granular structures are generated by various grain calculation mechanisms. In rough sets, for the same information table, different attribute adding sequence produces different sequential hierarchical structure, namely the rough granular structure. In rough granular structure, various order of attribute acquisition leads to different effects of solving uncertain problems. This leads to an interesting research topic: how to effectively evaluate the rough granular structures. This problem is solved from the perspective of knowledge distance in the paper. Firstly, the knowledge distance mentioned in our previous works is introduced and then a rough approximation space distance (RASD) is proposed to measure the difference between rough approximate space. On the basis of the knowledge distance mentioned above, the characteristic of rough granular structure (RGS) is investigated. In the rough granular structure, when solving uncertain problem, we expect to to maximize the uncertainty reduction as much as possible by using smaller knowledge space. Then, based on this idea and the above conclusions, an evaluation parameter λ is introduced under the constraint of attribute cost, and further, an evaluation model of rough granular structure is established.Finally,the effectiveness of this method is verified by an example.
- 参考文献/References:
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备注/Memo
收稿日期:2019-04-16。
基金项目:国家自然科学基金项目(61572091);贵州省教育厅科技人才成长项目(KY(2018)No.318)
作者简介:杨洁，副教授，博士，主要研究方向为粒计算、数据挖掘、粗糙集。参与国家自然科学基金项目2项，授权国家发明专利5项。发表学术论文20余篇;王国胤，教授，博士生导师，博士，重庆邮电大学研究生院院长、人工智能学院院长，主要研究方向为粗糙集、粒计算、认知计算、数据挖掘、智能信息处理。主持国家自然科学基金项目10 余项。入选“重庆高校创新团队”、 “重庆市首席专家工作室”和“国家级教学团队”；获国家级教学成果二等奖、重庆市自然科学一等奖、重庆市科技进步一等奖、重庆市教学成果一等奖和吴文俊人工智能科学技术奖科技进步一等奖等多项国家级/省部级教学和科技成果奖励。出版著作 10 余部，发表学术论文300 余篇;张清华，教授，博士生导师，博士，主要研究方向为粗糙集、模糊集、粒计算、三支决策。主持国家自然科学基金项目2项。发表学术论文40余篇
通讯作者:杨洁.E-mail:530966074@qq.com
更新日期/Last Update:
1900-01-01