Stream Convolution for Attribute Reduction of Concept Lattices

Author:

Xu Jianfeng12ORCID,Wu Chenglei2,Xu Jilin1,Liu Lan13,Zhang Yuanjian4ORCID

Affiliation:

1. School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China

2. School of Software, Nanchang University, Nanchang 330047, China

3. Nanchang Kindly (KDL) Medical Technology Co., Ltd., Nanchang 330000, China

4. China UnionPay Co., Ltd., Shanghai 201201, China

Abstract

Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

Funder

National Natural Science Foundations of China

China Scholarship

Jiangxi Natural Science Foundations

Jiangxi Training Program for Academic and Technical Leaders in Major Disciplines—Leading Talents Project

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

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