β-distribution reduction based on discernibility matrix in interval-valued decision systems

doi:10.11772/j.issn.1001-9081.2020040563

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1084-1092.DOI: 10.11772/j.issn.1001-9081.2020040563

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

β-distribution reduction based on discernibility matrix in interval-valued decision systems

LI Leitao^1,2, ZHANG Nan^1,2, TONG Xiangrong^1,2, YUE Xiaodong³

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:2020-05-04 Revised:2020-06-19 Online:2021-04-10 Published:2020-07-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (11801491), the Natural Science Foundation of Shandong Province (ZR2018BA004).

基于差别矩阵的区间值决策系统β分布约简

李磊涛^1,2, 张楠^1,2, 童向荣^1,2, 岳晓冬³

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:李磊涛（1997—），男，山东潍坊人，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，副教授，博士，CCF会员，主要研究方向：粗糙集、认知信息学、人工智能；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；岳晓冬（1981—），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。
基金资助:
国家自然科学基金资助项目（11801491）；山东省自然科学基金资助项目（ZR2018BA004）。

Abstract

Abstract: At present, the scale of interval type data is getting larger and larger. When using the classic attribute reduction method to process, the data needs to be preprocessed,thus leading to the loss of original information. To solve this problem, a β-distribution reduction algorithm of the interval-valued decision system was proposed. Firstly, the concept and the reduction target of β-distribution of the interval-valued decision system were given, and the proposed related theories were proved. Then, the discernibility matrix and discernibility function of β-distribution reduction were constructed for the above reduction target,and the β-distribution reduction algorithm of the interval-valued decision system was proposed. Finally,14 UCI datasets were selected for experimental verification. On Statlog dataset, when the similarity threshold is 0.6 and the number of objects is 100, 200, 400, 600 and 846 respectively, the average reduction length of the β-distribution reduction algorithm is 1.6, 2.2, 1.4, 2.4 and 2.6 respectively, the average reduction length of the Distribution Reduction Algorithm based on Discernibility Matrix(DRADM) is 2.0, 3.0, 3.0, 4.0 and 4.0 respectively, the average reduction length of the Maximum Distribution Reduction Algorithm based on Discernibility Matrix(MDRADM) is 2.0, 3.0, 3.0, 4.0 and 3.0 respectively. The effectiveness of the proposed β-distribution reduction algorithm is verified by experimental results.

Key words: interval-value, decision system, discernibility matrix, discernibility function, distribution reduction

摘要： 当前区间类型数据的规模越来越大，若采用传统的属性约简方法进行处理，就需要对数据进行预处理，而这会损失原始信息。针对上述问题，提出了区间值决策系统β分布的约简算法。首先，给出区间值决策系统β分布的概念和约简目标，并证明了提出的相关定理；然后，对于该约简目标构建了β分布约简的差别矩阵和差别函数，提出了区间值决策系统β分布约简算法；最后，使用14组UCI数据集进行实验验证。在数据集Statlog上，当相似度阈值为0.6，对象数目为100、200、400、600、846时，β分布约简算法的平均约简长度为1.6、2.2、1.4、2.4、2.6，基于差别矩阵的分布约简算法（DRADM）的平均约简长度为2.0、3.0、3.0、4.0、4.0，基于差别矩阵的最大分布约简算法（MDRADM）的平均约简长度为2.0、3.0、3.0、4.0、3.0。实验结果验证了所提β分布约简算法的有效性。

关键词: 区间值, 决策系统, 差别矩阵, 差别函数, 分布约简

CLC Number:

TP181

LI Leitao, ZHANG Nan, TONG Xiangrong, YUE Xiaodong. β-distribution reduction based on discernibility matrix in interval-valued decision systems[J]. Journal of Computer Applications, 2021, 41(4): 1084-1092.

李磊涛, 张楠, 童向荣, 岳晓冬. 基于差别矩阵的区间值决策系统β分布约简[J]. 计算机应用, 2021, 41(4): 1084-1092.

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β-distribution reduction based on discernibility matrix in interval-valued decision systems

基于差别矩阵的区间值决策系统β分布约简

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