Missing Data Imputation using Neural Cellular Automata
This addresses the issue of missing data for users of tabular datasets, representing an incremental improvement by applying NCA to a known bottleneck.
The paper tackles the problem of missing data imputation in tabular data by proposing a novel method based on Neural Cellular Automata (NCA), showing that it outperforms state-of-the-art methods in terms of imputation error and post-imputation performance.
When working with tabular data, missingness is always one of the most painful problems. Throughout many years, researchers have continuously explored better and better ways to impute missing data. Recently, with the rapid development evolution in machine learning and deep learning, there is a new trend of leveraging generative models to solve the imputation task. While the imputing version of famous models such as Variational Autoencoders or Generative Adversarial Networks were investigated, prior work has overlooked Neural Cellular Automata (NCA), a powerful computational model. In this paper, we propose a novel imputation method that is inspired by NCA. We show that, with some appropriate adaptations, an NCA-based model is able to address the missing data imputation problem. We also provide several experiments to evidence that our model outperforms state-of-the-art methods in terms of imputation error and post-imputation performance.