Abstract
The problem of missing values is often encountered in tasks such as machine learning, and imputation of missing values has become an important research content in incomplete data analysis. In this paper, we propose an attribute cross fitting model (ACFM) based on auto-associative neural network (AANN), which enhances the fitting of regression relations among attributes of incomplete data and reduces the dependence of imputation values on pre-filling values. Besides, we propose a model training scheme that takes missing values as variables and dynamically updates missing value variables based on optimization algorithm. The imputation accuracy is expected to be gradually improved through the dynamic adjustment of missing values. The experimental results verified the effectiveness of proposed method.
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Acknowledgement
This work was supported by National Key R&D Program of China under Grant 2018YFB1700200.
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Zhu, J., Zhang, L., Lai, X., Zhang, G. (2020). Imputation of Incomplete Data Based on Attribute Cross Fitting Model and Iterative Missing Value Variables. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_15
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DOI: https://doi.org/10.1007/978-3-030-64221-1_15
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