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A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application

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Abstract

Logistic regression model is a commonly used data correlation analysis model in statistical analysis methods which has been widely used in economics, medicine, sociology, psychology and other fields. Information fusion method based on granular computing is an important big data analysis method in which clustering ensemble method is an important branch. Clustering ensemble method can granulate a data set into a multi granular structure, integrate multiple clustering results, and obtain a better clustering effect than a single clustering algorithm. However it is only applicable to specific data sets and the clustering results have no practical significance, and therefore it is difficult to construct a consistent fusion function. To solve the above problems, this paper proposes a multi-granularity information fusion method based on logistic regression model and D-S evidence theory, and applies it to multi-attribute group decision-making. Firstly, the data set is granulated into multi granular structure by using logistic regression model, and the output result is the probability that each object belongs to each category at different granularities; Secondly, the output results are used to construct the mass functions of all objects at each granularity, and the Dempster synthesis formula in evidence theory is used to fuse the mass functions at each granularity. Then, a multi-granularity information fusion algorithm (LDIF) based on logistic regression model and D-S evidence theory is proposed, and the complexity and experimental analysis of the algorithm are carried out. Finally, the multi-granularity information fusion method based on logistic regression model and D-S evidence theory is applied to multi-attribute group decision-making. The results show that compared with other methods, this method has the advantages of simple calculation, convenient construction of mass function and high classification accuracy. It is very suitable for multi-attribute group decision-making problems and has excellent performance.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (62076088).

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Authors and Affiliations

  1. School of Mathematical Sciences, Hebei Normal University, Shijiazhuang, 050024, People’s Republic of China

    Huijuan Zhao & Jusheng Mi

  2. College of Science, North China University of Science and Technology, Tangshan, 063210, People’s Republic of China

    Huijuan Zhao

  3. Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang, 050043, People’s Republic of China

    Meishe Liang

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  1. Huijuan Zhao
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  2. Jusheng Mi
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  3. Meishe Liang
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Correspondence to Jusheng Mi.

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Zhao, H., Mi, J. & Liang, M. A multi-granularity information fusion method based on logistic regression model and Dempster-Shafer evidence theory and its application. Int. J. Mach. Learn. & Cyber. 13, 3131–3142 (2022). https://doi.org/10.1007/s13042-022-01584-w

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