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Efficient accuracy evaluation for multi-modal sensed data

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Abstract

Data accuracy is an important aspect in sensed data quality. Thus one necessary task for data quality management is to evaluate the accuracy of sensed data. However, to our best knowledge, neither measure nor effective methods for the accuracy evaluation are proposed for multi-typed sensed data. To address the problem for accuracy evaluation, we propose a systematic method. With MSE, a parameter to measure the accuracy in statistics, we design the accuracy evaluation framework for multi-modal data. Within this framework, we classify data types into three categories and develop accuracy evaluation algorithms for each category in cases of in presence and absence of true values. Extensive experimental results show the efficiency and effectiveness of our proposed framework and algorithms.

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Acknowledgments

This paper was partially supported by NGFR 973 Grant 2012CB316200 and NSFC Grant 61472099.

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Yan Zhang, Hongzhi Wang, Hong Gao & Jianzhong Li

Authors
  1. Yan Zhang
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  2. Hongzhi Wang
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  3. Hong Gao
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  4. Jianzhong Li
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Correspondence to Hongzhi Wang.

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Zhang, Y., Wang, H., Gao, H. et al. Efficient accuracy evaluation for multi-modal sensed data. J Comb Optim 32, 1068–1088 (2016). https://doi.org/10.1007/s10878-015-9920-8

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  • DOI: https://doi.org/10.1007/s10878-015-9920-8

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