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With the advent of the big data era, the data quality problem is becomingmore critical. Among many factors, data with missing values is one primaryissue, and thus developing effective imputation models is a key topic in theresearch community. Recently, a major research direction is to employ neuralnetwork models such as self-organizing mappings or automatic encoders forfilling missing values. However, these classical methods can hardly discoverinterrelated features and common features simultaneously among data attributes.Especially, it is a very typical problem for classical autoencoders that theyoften learn invalid constant mappings, which dramatically hurts the fillingperformance. To solve the above-mentioned problems, we propose amissing-value-filling model based on a feature-fusion-enhanced autoencoder. Wefirst incorporate into an autoencoder a hidden layer that consists ofde-tracking neurons and radial basis function neurons, which can enhance theability of learning interrelated features and common features. Besides, wedevelop a missing value filling strategy based on dynamic clustering that isincorporated into an iterative optimization process. This design can enhancethe multi-dimensional feature fusion ability and thus improves the dynamiccollaborative missing-value-filling performance. The effectiveness of theproposed model is validated by extensive experiments compared to a variety ofbaseline methods on thirteen data sets.

A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder