Incremental Learning Based on Granular Ball Rough Sets for Classification in Dynamic Mixed-Type Decision System

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 9; pp. 9319 - 9332
• Main Authors: Zhang, Qinghua, Wu, Chengying, Xia, Shuyin, Zhao, Fan, Gao, Man, Cheng, Yunlong, Wang, Guoyin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Classifiers; Computation; Computational modeling; Datasets; dynamic information systems; Granular computing; incremental learning; Indexes; Information systems; Knowledge discovery; Machine learning; Rough sets; Task analysis
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3237833

Granular computing, a new paradigm for solving large-scale and complex problems, has made significant progresses in knowledge discovery. Granular ball computing (GBC) is a novel granular computing method, which can rapidly generate scalable and robust information granules, that is, granular balls. However, a comprehensive index for measuring the performance of a granular ball does not exist. Furthermore, GBC lacks a mechanism to deal with dynamic decision systems. Therefore, in this study, the quality index of a granular ball is first formulated. Next, with this index, a novel granular ball rough sets model (GBRS) based on GBC is proposed. GBRS is more conducive to learning knowledge from uncertain datasets and more suited to incremental learning than the latest granular ball neighborhood rough sets model based on GBC. Subsequently, an incremental mechanism is introduced into GBRS, and two incremental learning models are developed for objects increasing in stream patterns and batch patterns, respectively. In the incremental learning process, three patterns of granular balls, that is, update, fusion, and split, were well studied when a set of objects was added to the decision system. Finally, to verify the effectiveness and efficiency, we apply GBRS and these two incremental learning models into classification tasks. Compared with four current state-of-the-art classification methods based on granular computing and four classical classifiers in machine learning, the proposed classifiers in this paper achieve higher classification accuracy as well as better efficiency on benchmark datasets.