A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

With the advent of the big data era, the data quality problem is becoming more and more crucial. 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 selforganizing mappings or automatic encoders for filling missing values. However, these classical methods can hardly discover correlation 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, thus dramatically hurting the filling performance. To solve the above problems, we propose and develop a missing-value-filling model based on a feature-fusion-enhanced autoencoder. We first design and incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability to learn correlated features and common features. Besides, we develop a missing value filling strategy based on dynamic clustering (MVDC) 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 our model is validated by experimental comparisons to many missing-value-filling methods that are tested on seven datasets with different missing rates.