Class overlap handling methods in imbalanced domain: A comprehensive survey

Kumar, Anil; Singh, Dinesh; Shankar Yadav, Rama

doi:10.1007/s11042-023-17864-8

Multimed Tools Appl

2024

DOI: 10.1007/s11042-023-17864-8

|View full text |Cite

Class overlap handling methods in imbalanced domain: A comprehensive survey

Anil Kumar,

Dinesh Singh,

Rama Shankar Yadav

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 221 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

Djafri

2025

Information Sciences

View full text Add to dashboard Cite

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

Djafri

2025

Information Sciences

View full text Add to dashboard Cite

Entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced dataset

Kumar,

Singh,

Yadav

2024

Expert Systems

View full text Add to dashboard Cite

Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap.

show abstract

Data-Centric Solutions for Addressing Big Data Veracity with Class Imbalance, High Dimensionality, and Class Overlapping

Bolívar,

García,

Alejo

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

An innovative strategy for organizations to obtain value from their large datasets, allowing them to guide future strategic actions and improve their initiatives, is the use of machine learning algorithms. This has led to a growing and rapid application of various machine learning algorithms with a predominant focus on building and improving the performance of these models. However, this data-centric approach ignores the fact that data quality is crucial for building robust and accurate models. Several dataset issues, such as class imbalance, high dimensionality, and class overlapping, affect data quality, introducing bias to machine learning models. Therefore, adopting a data-centric approach is essential to constructing better datasets and producing effective models. Besides data issues, Big Data imposes new challenges, such as the scalability of algorithms. This paper proposes a scalable hybrid approach to jointly addressing class imbalance, high dimensionality, and class overlapping in Big Data domains. The proposal is based on well-known data-level solutions whose main operation is calculating the nearest neighbor using the Euclidean distance as a similarity metric. However, these strategies may lose their effectiveness on datasets with high dimensionality. Hence, the data quality is achieved by combining a data transformation approach using fractional norms and SMOTE to obtain a balanced and reduced dataset. Experiments carried out on nine two-class imbalanced and high-dimensional large datasets showed that our scalable methodology implemented in Spark outperforms the traditional approach.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Class overlap handling methods in imbalanced domain: A comprehensive survey

Cited by 3 publications

References 221 publications

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

PRO-SMOTEBoost: An adaptive SMOTEBoost probabilistic algorithm for rebalancing and improving imbalanced data classification

Entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced dataset

Data-Centric Solutions for Addressing Big Data Veracity with Class Imbalance, High Dimensionality, and Class Overlapping

Contact Info

Product

Resources

About