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Optimal data fusion based on information quality function

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Abstract

Information quality based on entropy is defined to measure uncertainty of a probability distribution, which is an important criterion in the field of data fusion. For given probability distributions, how to obtain the reasonable result of high quality is still an open issue. Especially when given probability distributions are highly conflicted, the result of applying classic data fusion method is counter-intuitive. In this paper, an optimal data fusion method on the basis of genetic algorithm is proposed to resolve this issue and the result with minimal Gini entropy within a reasonable range can be obtained. This method can handle the probabilistic fusion problem of given sources high conflict effectively and the Gini entropy of result is relatively small. Genetic algorithm is applied in this method to handle data fusion problem in this paper. The effectiveness of this proposed method is illustrated and the advantages of this method are analysed compared with other fusion methods.

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Acknowledgements

The work is partially supported by National Natural Science Foundation of China (Program Nos. 61703338, 61671384), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2016JM6018).

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Shuang Liang, Xinyang Deng & Wen Jiang

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  1. Shuang Liang
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  2. Xinyang Deng
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  3. Wen Jiang
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Correspondence to Xinyang Deng.

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Liang, S., Deng, X. & Jiang, W. Optimal data fusion based on information quality function. Appl Intell 49, 3938–3946 (2019). https://doi.org/10.1007/s10489-019-01494-5

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