Author
Listed:
- Kusum Kumari Bharti
(Dr. B . R. Ambedkar National Institute of Technology)
- Ashutosh Tripathi
(Pandit Deendayal Energy University)
- Mohona Ghosh
(Indira Gandhi Delhi Technical University For Women)
Abstract
The issue of imbalanced datasets, i.e., uneven sample distribution among different classes causes training biases and degrades learning algorithm performance. In past, several solutions for data imbalance handling have been proposed but most of them focus on removing the majority class instances, leading to loss of important information. An alternate strategy to mitigate this issue that has been investigated in literature is minority class samples generation. However, generation of quality synthetic samples for minority class remains an open problem. In this study, a fusion of grey wolf optimizer (GWO) with artificial bee colony (ABC) is proposed to generate good representative samples of the minority class. The combination is analysed because GWO has good exploitation abilities, while ABC is good at exploration. The effectiveness of the proposed method is tested on 20 real-world benchmark datasets and on one real-life application, i.e., scam video classification on YouTube using standard assessment indicators. The performance of the proposed method is compared against 18 state-of-the-art data imbalance handling methods using three classification algorithms, i.e., support vector machine (SVM), k-nearest neighbours (KNN) and decision tree (DT). Our experimental results show an improvement in G-mean score on 18 out of 20 datasets with a maximum improvement of 8% for SVM, and on 17 out of 20 datasets with a maximum improvement of 10.7% for KNN and 6.3% for DT respectively. An improvement in AUC score is also seen on 17 out of 20 datasets for SVM and DT with a maximum improvement of 4.5% and 6% respectively, and on 16 out of 20 datasets for KNN with a maximum improvement of 7.7%. These results show that the proposed method is robust.
Suggested Citation
Kusum Kumari Bharti & Ashutosh Tripathi & Mohona Ghosh, 2024.
"A fused grey wolf and artificial bee colony model for imbalanced data classification problems,"
International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(8), pages 4085-4104, August.
Handle:
RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02412-w
DOI: 10.1007/s13198-024-02412-w
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
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:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02412-w. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.