JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (7): 37-51.doi: 10.6040/j.issn.1671-9352.4.2022.5896

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Dynamic neighborhood rough sets approaches for updating knowledge while attributes generalization

Chengxiang HU1(),Li ZHANG2,*(),Xiaoling HUANG1,Huibin WANG1   

  1. 1. School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, Anhui, China
    2. School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
  • Received:2022-07-26 Online:2023-07-20 Published:2023-07-05
  • Contact: Li ZHANG E-mail:chengxiang0550@163.com;zhangliml@suda.edu.cn

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Abstract:

We propose the matrix-based dynamic methods for updating three-way regions in neighborhood decision systems while attributes generalizing. First, we construct the matrix representation strategy of three-way regions for each decision class by means of neighborhood relation matrix and neighborhood distance matrix.Subsequently, considering the variation of the attributes, the updating strategies for related matrices are analyzed by using prior knowledge. Based on these strategies, we propose the matrix-based incremental methods for updating neighborhood three-way regions of each decision class. Finally, we evaluate the efficiency of the proposed incremental methods on public available data sets.

Key words: neighborhood rough set, dynamic updating, three-way regions, matrix

CLC Number: 

  • TP18

Table 1

Description information of experimental datasets"

编号 数据集 对象数 属性数 决策类数
1 Sonar 208 60 2
2 Ionosphere 351 34 2
3 Diabetic 1 151 19 2
4 Segmentation 2 310 19 7
5 Statlog 6 435 36 6
6 Musk-2 6 598 166 2

Table 2

The devision of adding attributes on experimental datasets"

编号 数据集 初始属性集 增量属性集
1 Sonar {a1, a2, …, a40} {a41, a42, …, a60}
2 Ionosphere {a1, a2, …, a20} {a21, a22, …, a34}
3 Diabetic {a1, a2, …, a10} {a11, a12, …, a19}
4 Segmentation {a1, a2, …, a10} {a11, a12, …, a19}
5 Statlog {a1, a2, …, a22} {a23, a24, …, a36}
6 Musk-2 {a1, a2, …, a120} {a121, a122, …, a166}

Fig.1

A comparison of DMUA and SMCN with different sizes of universe when adding attributes"

Fig.2

A comparison of DMUA and SMCN with different updating ratios of adding attributes"

Table 3

The devision of deleting arributes on experimental datasets"

编号 数据集 初始属性集 待删除属性集
1 Sonar {a1, a2, …, a60} {a51, a52, …, a60}
2 Ionosphere {a1, a2, …, a34} {a25, a26, …, a34}
3 Diabetic {a1, a2, …, a19} {a13, a14, …, a19}
4 Segmentation {a1, a2, …, a19} {a1, a2, …, a5}
5 Statlog {a1, a2, …, a36} {a1, a2, …, a10}
6 Musk-2 {a1, a2, …, a166} {a1, a2, …, a30}

Fig.3

A comparison of DMUD and SMCN with different sizes of universe when deleting attributes"

Fig.4

A comparison of DMUD and SMCN with different updating ratios of deleting attributes"

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