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Optimal scale selection by integrating uncertainty and cost-sensitive learning in multi-scale decision tables

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Abstract

Optimal scale selection is an important issue in the study of multi-scale decision tables. Most existing optimal scale selection methods have been designed from the perspective of consistency or uncertainty, and cost as well as user requirements or preferences in practical applications has not been considered. It is well known that the uncertainty of decision making in different levels of scale varies in sequential three-way decision models. Furthermore, test cost depends on the scale, and delayed decisions may cause delay cost. In practical applications, both uncertainty and cost are supposed to be considered. Therefore, it is worthwhile to introduce cost-sensitive learning into multi-scale decision tables and select the optimal scale by comprehensively considering uncertainty and cost. In this study, uncertainty is firstly quantified, and a novel cost constitution is defined in sequential three-way decision models. In addition, a multi-scale decision information system based on test cost and delay cost is proposed. Then, to obtain the optimal scale with the minimum uncertainty and cost, an optimal scale selection model is established with the constraint of user requirements. Furthermore, an improved optimal scale selection model considering user preferences is proposed by introducing the ideal solution to resolve conflicts among objectives. Finally, the effectiveness of the optimal scale selection model is verified through experiments, and a comparative experimental analysis demonstrates that the proposed model is more consistent with actual user requirements than existing models.

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China under Grant 2017YFC0 804002, the National Natural Science Foundation of China under Grants 61876201 and 61876027, and the Talent Development Project of Guizhou Province under Grant KY(2018)No.31.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xueqiu Zhang, Qinghua Zhang, Yunlong Cheng & Guoyin Wang

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  1. Xueqiu Zhang
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  2. Qinghua Zhang
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  3. Yunlong Cheng
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  4. Guoyin Wang
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Correspondence to Qinghua Zhang.

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Zhang, X., Zhang, Q., Cheng, Y. et al. Optimal scale selection by integrating uncertainty and cost-sensitive learning in multi-scale decision tables. Int. J. Mach. Learn. & Cyber. 11, 1095–1114 (2020). https://doi.org/10.1007/s13042-020-01101-x

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