Positive region preservation reduction based on multi-specific decision classes in incomplete decision systems

doi:10.11772/j.issn.1001-9081.2018091963

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1252-1260.DOI: 10.11772/j.issn.1001-9081.2018091963

• Artificial intelligence • Previous Articles Next Articles

Positive region preservation reduction based on multi-specific decision classes in incomplete decision systems

KONG Heqing^1,2, ZHANG Nan^1,2, YUE Xiaodong³, TONG Xiangrong^1,2, YU Tianyou^1,2

1. Key Laboratory for Data Science and Intelligence Technology of Shandong Higher Education Institutes(Yantai University), Yantai Shandong 264005, China;
2. School of Computer and Control Engineering, Yantai University, Yantai Shandong 264005, China;
3. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China

Received:2018-09-25 Revised:2018-11-27 Online:2019-05-10 Published:2019-05-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61403329, 61572418, 61702439, 61572419, 61502410), the Shandong Provincial Natural Science Foundation (ZR2016FM42, ZR2018BA004).

基于多特定决策类的不完备决策系统正域约简

孔贺庆^1,2, 张楠^1,2, 岳晓冬³, 童向荣^1,2, 于天佑^1,2

1. 数据科学与智能技术山东省高校重点实验室(烟台大学), 山东烟台 264005;
2. 烟台大学计算机与控制工程学院, 山东烟台 264005;
3. 上海大学计算机工程与科学学院, 上海 200444

通讯作者: 张楠
作者简介:孔贺庆(1995-),男,山东曲阜人,硕士研究生,主要研究方向:粗糙集、数据挖掘、机器学习;张楠(1979-),男,山东烟台人,讲师,博士,CCF会员,主要研究方向:粗糙集、认知信息学、人工智能;岳晓冬(1980-),男,山西太原人,副教授,博士,CCF会员,主要研究方向:机器学习、软计算、数据挖掘;童向荣(1975-),男,山东烟台人,教授,博士,CCF会员,主要研究方向:多Agent系统、数据挖掘、分布式人工智能;于天佑(1995-),男,山东邹城人,硕士研究生,主要研究方向:粗糙集、数据挖掘、人工智能。
基金资助:
国家自然科学基金资助项目（61403329，61572418，61702439，61572419，61502410）；山东省自然科学基金资助项目（ZR2016FM42，ZR2018BA004）。

Abstract

Abstract: The existing attribute reduction algorithms mostly focus on all decision classes in decision systems, but in actual decision process, decision makers may only focus on one or several decision classes in the decision systems. To solve this problem, a theoretical framework of positive region preservation reduction based on multi-specific decision classes in incomplete decision systems was proposed. Firstly, the positive region preservation reduction for single specific decision class in incomplete decision systems was defined. Secondly, the positive region preservation reduction for single specific decision class was extended to multi-specific decision classes, and the corresponding discernibility matrix and function were constructed. Thirdly, with related theorems analyzed and proved, an algorithm of Positive region preservation Reduction for Multi-specific decision classes reduction based on Discernibility Matrix in incomplete decision systems (PRMDM) was proposed. Finally, four UCI datasets were selected for experiments. On Teaching-assistant-evaluation, House, Connectionist-bench and Cardiotocography dataset, the average reduction length of Positive region preservation Reduction based on Discernibility Matrix in incomplete decision systems (PRDM) algorithm is 4.00, 13.00, 9.00 and 20.00 respectively while that of the PRMDM algorithm (with decision classes in the multi-specific decision classes is 2) is 3.00, 8.00, 8.00 and 18.00 respectively. The validity of PRMDM algorithm is verified by experimental results.

Key words: rough set, incomplete decision system, multi-specific decision classes, positive region preservation reduction, discernibility matrix

摘要： 现有的属性约简方法大部分关注决策系统中的所有决策类，而在实际决策过程中决策者往往仅关注决策系统中的一种或几种决策类。针对上述问题，提出基于多特定决策类的不完备决策系统正域约简的理论框架。首先，给出不完备决策系统单特定决策类正域约简的概念；第二，将单特定决策类正域约简推广到多特定决策类，构造了相应的差别矩阵及区分函数；第三，分析并证明了相关定理，提出基于差别矩阵的不完备决策系统多特定决策类正域约简算法（PRMDM）；最后，选取4组UCI数据集进行实验。在数据集Teaching-assistant-evaluation、House、Connectionist-bench和Cardiotocography上，基于差别矩阵的不完备决策系正域约简算法（PRDM）的平均约简长度分别为4.00、13.00、9.00和20.00，PRMDM算法（多特定决策类中决策类数目为2）的平均约简长度分别为3.00、8.00、8.00和18.00。实验结果验证了PRMDM算法的有效性。

关键词: 粗糙集, 不完备决策系统, 多特定决策类, 正域约简, 差别矩阵

CLC Number:

TP181

KONG Heqing, ZHANG Nan, YUE Xiaodong, TONG Xiangrong, YU Tianyou. Positive region preservation reduction based on multi-specific decision classes in incomplete decision systems[J]. Journal of Computer Applications, 2019, 39(5): 1252-1260.

孔贺庆, 张楠, 岳晓冬, 童向荣, 于天佑. 基于多特定决策类的不完备决策系统正域约简[J]. 计算机应用, 2019, 39(5): 1252-1260.

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Positive region preservation reduction based on multi-specific decision classes in incomplete decision systems

基于多特定决策类的不完备决策系统正域约简

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