Keep it simple: random oversampling for imbalanced data

Firuz Kamalov; Ho Hon Leung; Aswani Kumar Cherukuri

doi:10.1109/ASET56582.2023.10180891

Keep it simple: random oversampling for imbalanced data

Firuz Kamalov
, Ho Hon Leung
, Aswani Kumar Cherukuri

Department of Mathematical Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

34 Citations (Scopus)

Abstract

The issue of imbalanced data affects a wide range of applications. Despite a plethora of sophisticated sampling techniques for dealing with imbalanced data, the simple random oversampling (ROS) method remains a robust alternative. The goal of this paper is to compare the performance of ROS to the more advanced sampling algorithms. To this end, we conduct numerical experiments on multi-label data. The results of the experiments reveal that ROS outperforms several advanced sampling algorithms. Given the computational efficiency of ROS and its robust accuracy, we believe that it provides a good option for dealing with imbalanced data.

Original language	English
Title of host publication	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665454742
DOIs	https://doi.org/10.1109/ASET56582.2023.10180891
Publication status	Published - 2023
Event	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 - Dubai, United Arab Emirates Duration: Feb 20 2023 → Feb 23 2023

Publication series

Name	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023

Conference

Conference	2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	2/20/23 → 2/23/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

data mining
imbalanced data
machine learning
random oversampling

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Biomedical Engineering
Control and Optimization
Artificial Intelligence
Computer Science Applications
Decision Sciences (miscellaneous)
Fuel Technology

Access to Document

10.1109/ASET56582.2023.10180891

Cite this

Kamalov, F., Leung, H. H., & Cherukuri, A. K. (2023). Keep it simple: random oversampling for imbalanced data. In 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023 (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASET56582.2023.10180891

Keep it simple: random oversampling for imbalanced data. / Kamalov, Firuz; Leung, Ho Hon; Cherukuri, Aswani Kumar.
2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kamalov, F, Leung, HH & Cherukuri, AK 2023, Keep it simple: random oversampling for imbalanced data. in 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, Institute of Electrical and Electronics Engineers Inc., 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, Dubai, United Arab Emirates, 2/20/23. https://doi.org/10.1109/ASET56582.2023.10180891

Kamalov F, Leung HH, Cherukuri AK. Keep it simple: random oversampling for imbalanced data. In 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2023 Advances in Science and Engineering Technology International Conferences, ASET 2023). doi: 10.1109/ASET56582.2023.10180891

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abstract = "The issue of imbalanced data affects a wide range of applications. Despite a plethora of sophisticated sampling techniques for dealing with imbalanced data, the simple random oversampling (ROS) method remains a robust alternative. The goal of this paper is to compare the performance of ROS to the more advanced sampling algorithms. To this end, we conduct numerical experiments on multi-label data. The results of the experiments reveal that ROS outperforms several advanced sampling algorithms. Given the computational efficiency of ROS and its robust accuracy, we believe that it provides a good option for dealing with imbalanced data.",

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AB - The issue of imbalanced data affects a wide range of applications. Despite a plethora of sophisticated sampling techniques for dealing with imbalanced data, the simple random oversampling (ROS) method remains a robust alternative. The goal of this paper is to compare the performance of ROS to the more advanced sampling algorithms. To this end, we conduct numerical experiments on multi-label data. The results of the experiments reveal that ROS outperforms several advanced sampling algorithms. Given the computational efficiency of ROS and its robust accuracy, we believe that it provides a good option for dealing with imbalanced data.

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ER -

Keep it simple: random oversampling for imbalanced data

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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