IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7097-d1131143.html
   My bibliography  Save this article

Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

Author

Listed:
  • Shahnawaz Ayoub

    (Department of Computer Science and Engineering, Shri Venkateshwara University, NH-24, Venkateshwara Nagar, Gajraula 244236, Uttar Pradesh, India)

  • Yonis Gulzar

    (Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Jaloliddin Rustamov

    (Health Data Science Lab, Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

  • Abdoh Jabbari

    (Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Faheem Ahmad Reegu

    (Department of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Sherzod Turaev

    (Department of Computer Science & Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates)

Abstract

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.

Suggested Citation

  • Shahnawaz Ayoub & Yonis Gulzar & Jaloliddin Rustamov & Abdoh Jabbari & Faheem Ahmad Reegu & Sherzod Turaev, 2023. "Adversarial Approaches to Tackle Imbalanced Data in Machine Learning," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7097-:d:1131143
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7097/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/9/7097/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    2. Khalied Albarrak & Yonis Gulzar & Yasir Hamid & Abid Mehmood & Arjumand Bano Soomro, 2022. "A Deep Learning-Based Model for Date Fruit Classification," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    3. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Poonam Dhiman & Amandeep Kaur & V. R. Balasaraswathi & Yonis Gulzar & Ali A. Alwan & Yasir Hamid, 2023. "Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    2. Xinyu Jia & Xueqin Jiang & Zhiyong Li & Jiong Mu & Yuchao Wang & Yupeng Niu, 2023. "Application of Deep Learning in Image Recognition of Citrus Pests," Agriculture, MDPI, vol. 13(5), pages 1-19, May.
    3. Bashar Igried & Shadi AlZu’bi & Darah Aqel & Ala Mughaid & Iyad Ghaith & Laith Abualigah, 2023. "An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms," Agriculture, MDPI, vol. 13(4), pages 1-20, April.
    4. Yane Li & Ying Wang & Dayu Xu & Jiaojiao Zhang & Jun Wen, 2023. "An Improved Mask RCNN Model for Segmentation of ‘Kyoho’ ( Vitis labruscana ) Grape Bunch and Detection of Its Maturity Level," Agriculture, MDPI, vol. 13(4), pages 1-18, April.
    5. Yanlei Xu & Zhiyuan Gao & Yuting Zhai & Qi Wang & Zongmei Gao & Zhao Xu & Yang Zhou, 2023. "A CNNA-Based Lightweight Multi-Scale Tomato Pest and Disease Classification Method," Sustainability, MDPI, vol. 15(11), pages 1-17, May.
    6. Yonis Gulzar & Zeynep Ünal & Hakan Aktaş & Mohammad Shuaib Mir, 2023. "Harnessing the Power of Transfer Learning in Sunflower Disease Detection: A Comparative Study," Agriculture, MDPI, vol. 13(8), pages 1-17, July.
    7. Yanxin Hu & Gang Liu & Zhiyu Chen & Jiaqi Liu & Jianwei Guo, 2023. "Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation," Agriculture, MDPI, vol. 13(9), pages 1-22, August.
    8. Ewa Ropelewska & Dorota E. Kruczyńska & Ahmed M. Rady & Krzysztof P. Rutkowski & Dorota Konopacka & Karolina Celejewska & Monika Mieszczakowska-Frąc, 2023. "Evaluating the Classification of Freeze-Dried Slices and Cubes of Red-Fleshed Apple Genotypes Using Image Textures, Color Parameters, and Machine Learning," Agriculture, MDPI, vol. 13(3), pages 1-16, February.
    9. Yonis Gulzar, 2023. "Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    10. Yang Chen & Xiaoyulong Chen & Jianwu Lin & Renyong Pan & Tengbao Cao & Jitong Cai & Dianzhi Yu & Tomislav Cernava & Xin Zhang, 2022. "DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification," Agriculture, MDPI, vol. 12(12), pages 1-22, November.
    11. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    12. Mohammed Al-Naeem & M M Hafizur Rahman & Anuradha Banerjee & Abu Sufian, 2023. "Support Vector Machine-Based Energy Efficient Management of UAV Locations for Aerial Monitoring of Crops over Large Agriculture Lands," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    13. Lu Lu & Wei Liu & Wenbo Yang & Manyu Zhao & Tinghao Jiang, 2022. "Lightweight Corn Seed Disease Identification Method Based on Improved ShuffleNetV2," Agriculture, MDPI, vol. 12(11), pages 1-18, November.
    14. Mahdieh Parsaeian & Mohammad Rahimi & Abbas Rohani & Shaneka S. Lawson, 2022. "Towards the Modeling and Prediction of the Yield of Oilseed Crops: A Multi-Machine Learning Approach," Agriculture, MDPI, vol. 12(10), pages 1-23, October.
    15. Ali Hakem Alsaeedi & Ali Mohsin Al-juboori & Haider Hameed R. Al-Mahmood & Suha Mohammed Hadi & Husam Jasim Mohammed & Mohammad R. Aziz & Mayas Aljibawi & Riyadh Rahef Nuiaa, 2023. "Dynamic Clustering Strategies Boosting Deep Learning in Olive Leaf Disease Diagnosis," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    16. Normaisharah Mamat & Mohd Fauzi Othman & Rawad Abdulghafor & Ali A. Alwan & Yonis Gulzar, 2023. "Enhancing Image Annotation Technique of Fruit Classification Using a Deep Learning Approach," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    17. Sonam Aggarwal & Sheifali Gupta & Deepali Gupta & Yonis Gulzar & Sapna Juneja & Ali A. Alwan & Ali Nauman, 2023. "An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
    18. Younés Noutfia & Ewa Ropelewska, 2022. "Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ ( Phoenix dactylifera L.) Using Image Analysis and Quality Attribute Measurements," Agriculture, MDPI, vol. 13(1), pages 1-12, December.
    19. Yujia Zhang & Luteng Zhong & Yu Ding & Hongfeng Yu & Zhaoyu Zhai, 2023. "ResViT-Rice: A Deep Learning Model Combining Residual Module and Transformer Encoder for Accurate Detection of Rice Diseases," Agriculture, MDPI, vol. 13(6), pages 1-17, June.
    20. Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7097-:d:1131143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.