In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.
@article{arxiv.1612.05730,
title = {Towards Wide Learning: Experiments in Healthcare},
author = {Snehasis Banerjee and Tanushyam Chattopadhyay and Swagata Biswas and Rohan Banerjee and Anirban Dutta Choudhury and Arpan Pal and Utpal Garain},
journal= {arXiv preprint arXiv:1612.05730},
year = {2016}
}
Comments
4 pages, Machine Learning for Health Workshop, NIPS 2016