A Simplistic Machine Learning Approach to Contact Tracing
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Abstract (arXiv)
This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs). The modified challenge excludes the time calculation (too long) aspect. By handcrafting features from phone instrumental data we develop two machine learning models, a GBM and an MLP, to estimate distance between two phones. Our method is able to outperform the leading NIST challenge result by the Hong Kong University of Science and Technology (HKUST) by a significant margin.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 3.
4 papers.
- 2022Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifierscited by 1
- 2021TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boostingcited by 0
- 2022A Machine Learning Approach to Digital Contact Tracing: TC4TL Challengecited by 0
- 2022Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readingscited by 0
- 2022Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers24 Aug 2022
- 2022Automatic Contact Tracing using Bluetooth Low Energy Signals and IMU Sensor Readings13 Jun 2022
- 2022A Machine Learning Approach to Digital Contact Tracing: TC4TL Challenge8 Mar 2022
- 2021TsFeX: Contact Tracing Model using Time Series Feature Extraction and Gradient Boosting29 Nov 2021