Abstract
With the increasing number of machine learning parameters, the requirements on data quantity are getting higher and higher to train a good model. The choice of methods and the optimization of parameters can improve the model while the quality and quantity of the data determine the upper limit of the model. However, in realistic scenarios, it is quite challenging to get a lot of tag data. Therefore, it is natural to realize data augmentation by transforming the original data. We use three methods for data augmentation on different scales of original data in solving the crime prediction problem based on the description of the cases, and find that the effects of data augmentation are different for different models and different fundamental data quantities.
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Acknowledgment
The work is supported in part by the National Key Research and Development Program of China (2016YFC0800805) and the National Natural Science Foundation of China (61472176, 61772014).
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Yan, G., Li, Y., Zhang, S., Chen, Z. (2019). Data Augmentation for Deep Learning of Judgment Documents. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_19
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DOI: https://doi.org/10.1007/978-3-030-36204-1_19
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