[1]唐玉凯,张楠,童向荣,等.不完备决策系统下的多特定类广义决策约简[J].智能系统学报,2019,14(6):1199-1208.[doi:10.11992/tis.201905059]
TANG Yukai,ZHANG Nan,TONG Xiangrong,et al.The multi-class-specific generalized decision preservation reduction in incomplete decision systems[J].CAAI Transactions on Intelligent Systems,2019,14(6):1199-1208.[doi:10.11992/tis.201905059]
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TANG Yukai,ZHANG Nan,TONG Xiangrong,et al.The multi-class-specific generalized decision preservation reduction in incomplete decision systems[J].CAAI Transactions on Intelligent Systems,2019,14(6):1199-1208.[doi:10.11992/tis.201905059]
不完备决策系统下的多特定类广义决策约简
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第6期
页码:
1199-1208
栏目:
学术论文—机器感知与模式识别
出版日期:
2019-11-05
- Title:
- The multi-class-specific generalized decision preservation reduction in incomplete decision systems
- Keywords:
- rough sets; attribute reduction; incomplete; decision systems; tolerance relation; multi-class-specific; generalized decision preservation reduction; discernibility matrix
- 分类号:
- TP181
- 摘要:
- 属性约简是粗糙集理论研究中最重要的领域之一。经典的不完备决策系统广义决策约简关注决策系统中的所有决策类，而在实际应用中，决策者往往只关注一个或者几个特定决策类。针对以上问题，提出基于多特定类的不完备决策系统广义决策约简理论框架。首先，定义了单特定类的不完备决策系统广义决策约简的相关概念，提出并证明相关定理，构造相应差别矩阵和区分函数。其次，将单特定类的广义决策约简推广到多特定类，提出基于差别矩阵的多特定类的不完备决策系统广义决策约简算法。最后，采用6组UCI数据集进行实验。实验结果表明，相对全部决策类数量，当选定特定类数量较少时，平均约简长度有不同程度的缩短，占用空间有所减小，约简效率有不同程度的提升。
- Abstract:
- Attribute reduction has an important place in rough set theory. The method of classical generalized decision preservation reduction in incomplete decision systems is to find the reducts of all decision classes. In practical applications, however, the decision makers may focus on one or several decision classes. To fill this gap, the theoretical framework of multi-class-specific generalized decision preservation reduction in incomplete decision systems is proposed. First, the single-class-specific generalized decision preservation reduction in incomplete decision systems is defined. Related theorems are proposed and proven, and the corresponding discernibility matrix and function are constructed. Then, the single-class-specific generalized decision preservation reduction is extended to the multi-class-specific generalized decision preservation reduction in incomplete decision systems. The algorithm of the multi-class-specific generalized decision preservation reduction based on discernibility matrix in incomplete decision systems (MGDRDM) is proposed. Finally, six datasets from UCI were used for experiments. The experimental results show that when the number of selected specific classes is less than all the decision classes, the average length of reducts will be shortened to varying degrees, the space used will be reduced, and the time efficiency will be roughly improved.
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备注/Memo
收稿日期:2019-05-28。
基金项目:国家自然科学基金项目（61572418，61572419，61873117，61403329）；山东省自然科学基金项目（ZR2018BA004，ZR2016FM42）.
作者简介:唐玉凯,男,1996年生,硕士研究生,主要研究方向为粗糙集、数据挖掘与机器学习;张楠,男,1979年生,讲师,主要研究方向为粗糙集、认知信息学与人工智能;童向荣,男,1975年生,教授,主要研究方向为多Agent系统、分布式人工智能与数据挖掘技术。主持国家自然科学基金面上项目2项,发表学术论文50余篇。
通讯作者:张楠.E-mail:zhangnan0851@163.com
更新日期/Last Update:
2019-12-25