A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder

TL;DR

This paper introduces a feature fusion-enhanced autoencoder model for missing value imputation, combining de-tracking and radial basis neurons with dynamic clustering to improve feature learning and filling accuracy.

Contribution

It proposes a novel autoencoder architecture with specialized neurons and a dynamic clustering strategy for more effective missing data imputation.

Findings

Outperforms baseline methods on thirteen datasets

Enhances feature learning through de-tracking and RBF neurons

Improves dynamic collaborative missing-value filling

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.