Abstract
Current research on the classification for uncertain data mainly focuses on the structural changes of the classification algorithms. Existing methods have achieved encouraging results; however, they do not take an effective trade-off between accuracy and running time, and they do not have good portability. This paper proposed a new framework to solve the classification problem of uncertain data from data processing point. The proposed algorithm represents the distribution of raw data by a sampling method, which means that the uncertain data are converted into determined data. The proposed framework is suitable for all classifiers, and then, XGBoost is adopted as a specific classifier in this paper. The experimental results show that the proposed method is an effective way of handling the classification problem for uncertain data.
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Acknowledgments
This research work is funded by the National Key Research and Development Project of China (2016YFB0801003).
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Huang, J., Li, Y., Qi, K., Li, F. (2020). An Efficient Classification Method of Uncertain Data with Sampling. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_43
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DOI: https://doi.org/10.1007/978-981-13-6504-1_43
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