Abstract
Sensor fusion is the process of combining sensor readings from disparate resources so that the resulting information is more accurate and complete. The key challenge in sensor fusion arises from the inherent imperfection of data, commonly caused by sampling error, network respond time, imprecise measurement, and unreliable resources. Therefore, data fusion methods need to be advanced to address various aspects of data imperfections. In this paper, we first propose a novel unified data fusion framework based on rough set theory to systematically represent data granularity and imprecision. Then, we develop a cost-driven adaptive learning algorithm that can infer the optimal threshold values from data to obtain minimum cost. Our experimental study demonstrates the framework’s effectiveness and validity.
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References
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Williams, M., Wilson, R., Hancock, E.: Multi-sensor fusion with Bayesian inference. Comput. Anal. Images Patterns 1296, 25–32 (2005)
Roussel, S., Bellon-Maurel, V., Roger, J., Grenier, P.: Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties. Chemometr. Intell. Lab. Syst. 65, 209–219 (2003)
Basir, O., Yuan, X.H.: Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory. Inf. Fusion 8, 379–386 (2007)
Blank, S., Fohst, T., Berns, K.: A fuzzy approach to low level sensor fusion with limited system knowledge. In: 13th Conference on Information Fusion, pp. 1–7 (2010)
Jetto, L., Longhi, S., Vitali, D.: Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter. Control Eng. Pract. 7, 763–771 (1999)
Yao, Y.Y., Wong, S.K.M., Lingras, P.: A decision-theoretic rough set model. In: Ras, Z.W., Zemankova, M., Emrich, M.L. (eds.) Methodologies for Intelligent Systems 5, pp. 17–24. North-Holland, New York (1990)
Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180, 341–353 (2010)
Robnik-ikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)
https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Acknowledgements
The work was supported by the Enhancement Research Grant (ERG) from Office of Research Administration (SHSU).
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Zhou, B., Cho, H., Mansfield, A. (2017). Robust Sensor Data Fusion Through Adaptive Threshold Learning. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_32
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DOI: https://doi.org/10.1007/978-3-319-60042-0_32
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