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An adaptive sequential three-way decision-making model via dynamic fusion of incomplete mixed data considering misclassification penalty from holistic and incremental perspectives
Journal article   Peer reviewed

An adaptive sequential three-way decision-making model via dynamic fusion of incomplete mixed data considering misclassification penalty from holistic and incremental perspectives

Jiali Zhang, Yan Tu, Zhongyong Wan, Linqi Cheng and Benjamin Lev
Information sciences, v 727, 122785
Feb 2026
DOI: https://doi.org/10.1016/j.ins.2025.122785

Abstract

Sequential three-way decision Incomplete mixed data Holistic and incremental perspectives Adaptive threshold pair Dynamic fusion
Driven by the increasing complexity of modern data environments, the challenge of making accurate and efficient decisions in uncertain contexts has become a prominent research topic. Inspired by this, a sequential three-way decision model is proposed to dynamically fuse incomplete mixed data from both static and dynamic perspectives in this paper. Specifically, complete datasets are firstly utilized in the static perspective, employing an attention-based method to evaluate the importance of attributes, partition attribute sets, and systematically determine the order of attribute analysis. In the dynamic perspective, data processing in incremental environments is focused on, and adaptive threshold pairs are introduced. Subsequently, two distinct metrics, namely matching coefficients and Gaussian functions, are adopted to compute similarities for categorical and numerical data respectively. Moreover, four T-norm fusion methods are integrated, which are Minimum T-norm, Product T-norm, Lukasiewicz T-norm, and Cosine T-norm. In addition, a crucial cost function is constructed to incorporate hierarchical importance and misclassification penalties. Finally, comparative experiments are conducted on six datasets to evaluate our model against existing methods. Experimental results show that our method can effectively reduce decision-making costs while maintaining decision accuracy. In conclusion, this study provides effective solutions for dynamic data fusion and multi-stage decision-making in complex environments, offering significant theoretical and practical importance.

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Web of Science research areas
Computer Science, Information Systems
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