ItemConference Paper

SMOTE-Based Sampling for Addressing Class Imbalance

Title
SMOTE-Based Sampling for Addressing Class Imbalance
Creator
Chaudhary, Shweta; Parashar, Jyoti; Malik, Nisar Ahmad; Ali, Shalbbya; Upreti, Kamal; Vats, Prashant
Description
Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalancewhere one class is underrepresented relative to anotherpresents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Electrical Engineering;Volume;1276;pp.63-78
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
ADASYN; AUC-ROC; Balanced Data; Classification; Dataset; Imbalanced Data; Oversampling; SMOTE; Under Sampling
Coverage
Chaudhary S., Department of Computer Science and Engineering, JIMS Engineering Management Technical Campus, Uttar Pradesh, Greater Noida, India; Parashar J., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India; Malik N.A., Department of Information Technology, Government Degree College Kulgam, Jammu and Kashmir, India; Ali S., Department of Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Technology and Management, New Delhi, India; Upreti K., Department of Computer Science, Christ (Deemed to Be University), Delhi NCR, Ghaziabad, India; Vats P., Department of CSE, SCSE, Manipal University Jaipur, Rajasthan, Jaipur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18761100; ISBN: 978-981978463-9;
Format
online
Language
English
Type
Conference paper

Citation

Chaudhary, Shweta; Parashar, Jyoti; Malik, Nisar Ahmad; Ali, Shalbbya; Upreti, Kamal; Vats, Prashant, “SMOTE-Based Sampling for Addressing Class Imbalance,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25669.

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