Cost-Aware Ensemble Learning Approach for Overcoming Noise in Labeled Data

Abdulrahman Gharawi, Jumana Alsubhi, Lakshmish Ramaswamy

2023

Abstract

Machine learning models have demonstrated exceptional performance in various applications as a result of the emergence of large labeled datasets. Although there are many available datasets, acquiring high-quality labeled datasets is challenging since it involves huge human supervision or expert annotation, which are extremely labor-intensive and time-consuming. Since noisy datasets can affect the performance of machine learning models, acquiring high-quality datasets without label noise becomes a critical problem. However, it is challenging to significantly decrease label noise in real-world datasets without hiring expensive expert annotators. Based on extensive testing and research, this study examines the impact of different levels of label noise on the accuracy of machine learning models. It also investigates ways to cut labeling expenses without sacrificing required accuracy.

Paper Citation

in Harvard Style

Gharawi A., Alsubhi J. and Ramaswamy L. (2023). Cost-Aware Ensemble Learning Approach for Overcoming Noise in Labeled Data. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 381-388. DOI: 10.5220/0011675200003393

in Bibtex Style

@conference{icaart23,
author={Abdulrahman Gharawi and Jumana Alsubhi and Lakshmish Ramaswamy},
title={Cost-Aware Ensemble Learning Approach for Overcoming Noise in Labeled Data},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={381-388},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011675200003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Cost-Aware Ensemble Learning Approach for Overcoming Noise in Labeled Data
SN - 978-989-758-623-1
AU - Gharawi A.
AU - Alsubhi J.
AU - Ramaswamy L.
PY - 2023
SP - 381
EP - 388
DO - 10.5220/0011675200003393