Affiliations: [a] School of Physics and Electronic Science, Zunyi Normal University, Zunyi, China
| [b] Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| [c] National Pilot School of Software, Yunnan University, Kunming, China
Correspondence:
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Corresponding author. Jie Yang, School of Physics and Electronic Science, Zunyi Normal University, Zunyi 563002, China. Tel.: +86 13628525827; E-mail: [email protected].
Abstract: Based on the granular computing and three-way decisions theory, the sequential three-way decisions (S3WD) model implements the idea of progressive computing. However, almost S3WD models are established based on labeled information system, and there is still a lack of S3WD model for processing unlabeled information system (UIS). In this paper, to solve the issue of given accepted number for UIS, a data-driven sequential three-way decisions (DDS3WD) model is proposed. Firstly, from the perspective of similarity computed by TOPSIS, a general three-way decisions model for UIS based on decision risk is presented and its shortcomings are analyzed. Then, a concept of optimal density difference is defined to establish the DDS3WD model for UIS by updating attributes. Finally, the related experiments show that DDS3WD is feasible and effective for dealing with UIS under the condition of given accepted number of objects.
Keywords: Sequential three-way decisions, unlabeled information system, data-driven, optimal density difference
