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On the class overlap problem in imbalanced data classification
- Source :
- Knowledge-Based Systems. 212:106631
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Class imbalance is an active research area in the machine learning community. However, existing and recent literature showed that class overlap had a higher negative impact on the performance of learning algorithms. This paper provides detailed critical discussion and objective evaluation of class overlap in the context of imbalanced data and its impact on classification accuracy. First, we present a thorough experimental comparison of class overlap and class imbalance. Unlike previous work, our experiment was carried out on the full scale of class overlap and an extreme range of class imbalance degrees. Second, we provide an in-depth critical technical review of existing approaches to handle imbalanced datasets. Existing solutions from selective literature are critically reviewed and categorised as class distribution-based and class overlap-based methods. Emerging techniques and the latest development in this area are also discussed in detail. Experimental results in this paper are consistent with existing literature and show clearly that the performance of the learning algorithm deteriorates across varying degrees of class overlap whereas class imbalance does not always have an effect. The review emphasises the need for further research towards handling class overlap in imbalanced datasets to effectively improve learning algorithms’ performance.
- Subjects :
- Information Systems and Management
Computer science
business.industry
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
Imbalanced data
Class (biology)
Management Information Systems
Critical discussion
Range (mathematics)
Artificial Intelligence
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Subjects
Details
- ISSN :
- 09507051
- Volume :
- 212
- Database :
- OpenAIRE
- Journal :
- Knowledge-Based Systems
- Accession number :
- edsair.doi...........0190d9d97df559ba5540f3b8e62f61c5