Journal
INFORMATION SCIENCES
Volume 519, Issue -, Pages 382-406Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.12.036
Keywords
Knowledge discovery; Multigranulation; Matrix; Incremental learning; Approximations
Categories
Funding
- Natural Science Foundation of China [61373093, 61602327]
- Natural Science Foundation of Educational Commission of Anhui Province of China [1808085MF170, KJ2018A0432, KJ2018803]
- Collaborative Innovation Center of Novel Software Technology and Industrialization
- Six Talent Peak Project of Jiangsu Province [XYDXX-054]
- Soochow Scholar Project
- Postgraduate Research and Practice Innovation Program of Jiangsu Province [KYCX19_1929]
- Natural Science Foundation of the Jiangsu Higher Education Institutions of China [19KJA550002]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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Rough approximations play a significant role in rule extraction and decision making. However, re-scanning the entire data set to update the approximations is time-consuming due to the dynamic characteristics of objects in a neighborhood multigranulation space. In order to reduce the computational time, the neighborhood multigranulation approximations need to be updated in an incremental manner based on previously saved knowledge. Therefore, in this study, we establish a dynamic framework for maintaining the positive, boundary, and negative regions in neighborhood multigranulation spaces when adding or deleting objects from the matrix perspective. First, we explore the incremental mechanisms for updating relevant matrices when adding or deleting multiple objects. Based on the proposed mechanisms, we design the corresponding dynamic algorithms to incrementally update the positive, boundary, and negative regions. Finally, we conduct empirical experiments on benchmark UCI data sets to assess the feasibility and efficiency of our updating algorithms, which demonstrate their promising performance. (C) 2020 Elsevier Inc. All rights reserved.
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