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A missing value filling model based on feature fusion enhanced autoencoder

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Abstract

With the advent of the big data era, the data quality problem is becoming more critical. Among many factors, data with missing values is one primary issue, and thus developing effective imputation models is a key topic in the research community. Recently, a major research direction is to employ neural network models such as self-organizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover interrelated features and common features simultaneously among data attributes. Especially, it is a very typical problem for classical autoencoders that they often learn invalid constant mappings, which dramatically hurts the filling performance. To solve the above-mentioned problems, we propose a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering that is incorporated into an iterative optimization process. This design can enhance the multi-dimensional feature fusion ability and thus improves the dynamic collaborative missing-value-filling performance. The effectiveness of the proposed model is validated by extensive experiments compared to a variety of baseline methods on thirteen data sets.

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Notes

  1. This paper is an extended version of [11], which has been accepted for presentation at the 15th International FLINS Conferences on Machine learning, Multi agent and Cyber physical systems (FLINS2022).

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Acknowledgements

This work is supported by the National Key R &D Program of China (No.2020AAA0105101), the National Natural Science Foundation of China (No.62276215, 62176221, 61976247)

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Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, People’s Republic of China

    Xinyao Liu, Shengdong Du, Tianrui Li, Fei Teng & Yan Yang

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  1. Xinyao Liu
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  2. Shengdong Du
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Correspondence to Shengdong Du.

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Liu, X., Du, S., Li, T. et al. A missing value filling model based on feature fusion enhanced autoencoder. Appl Intell 53, 24931–24946 (2023). https://doi.org/10.1007/s10489-023-04892-y

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