Local temporal-spatial multi-granularity learning for sequential three-way granular computing

Based on multiple levels of granularity, the notion of sequential three-way granular computing focuses on a multiple stages of thinking, problem-solving, and information processing in threes. This paper interprets, represents, and implements sequential three-way granular computing by a framework of temporal-spatial multi-granularity learning, which is described with the temporality of data and the spatiality of parameters. In real-world decision-making, such a sequential approach is useful to make faster decisions for some objects with the lower cost of decision process and the acceptable accuracy when information is insufficient or unavailable. However, the cost of time-consuming computation for hierarchical multilevel granularity is our concern. To address this issue, we utilize a local strategy to accelerate a sequence of neighborhood-based granulation induced by Gaussian kernel function. Subsequently, local three-way decision rules are investigated based on the Bayesian minimum risk criterion. Moreover, by the construction of a novel local trisection model, we propose a local sequential approach of three-way granular computing under a temporal-spatial multilevel granular structure. Finally, a series of comparative experiments between global and local perspectives is carried out to verify the effectiveness of our proposed models. (C) 2020 Elsevier Inc. All rights reserved.