Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance

Matthias Blohm, Marc Hanussek, Maximilien Kintz

Abstract

Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.

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Paper Citation


in Harvard Style

Blohm M., Hanussek M. and Kintz M. (2021). Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1131-1136. DOI: 10.5220/0010331411311136


in Bibtex Style

@conference{icaart21,
author={Matthias Blohm and Marc Hanussek and Maximilien Kintz},
title={Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1131-1136},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010331411311136},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance
SN - 978-989-758-484-8
AU - Blohm M.
AU - Hanussek M.
AU - Kintz M.
PY - 2021
SP - 1131
EP - 1136
DO - 10.5220/0010331411311136