Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks
Abstract
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have tried using various data imputation techniques to fill in the missing data, or training neural networks (NNs) with the missing data. In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss. We evaluate this approach on a series of benchmark datasets and show promising improvements even with simple imputation techniques. We attribute this to learning through clusters of similar features in our model architecture. The source code is available at https://github.com/usarawgi911/Robustness-to-Missing-Features
Cite
@article{arxiv.2011.09596,
title = {Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks},
author = {Rishab Khincha and Utkarsh Sarawgi and Wazeer Zulfikar and Pattie Maes},
journal= {arXiv preprint arXiv:2011.09596},
year = {2020}
}
Comments
To appear at AAAI 2021 Student Abstract