HS-Gen: a hypersphere-constrained generation mechanism to improve synthetic minority oversampling for imbalanced classification

Mitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE is a well-known method to tackle this task by modifying class distribution and generating synthetic instances. However, most of the SMOTE-based methods focus on the phase of data selection,...

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Bibliographic Details
Published in:Complex & intelligent systems 2023-08, Vol.9 (4), p.3971-3988
Main Authors: He, Zuowei, Tao, Jiaqing, Leng, Qiangkui, Zhai, Junchang, Wang, Changzhong
Format: Article
Language:English
Subjects:
Algorithms
Classification
Classifiers
Complexity
Computational Intelligence
Data Structures and Information Theory
Datasets
Embedding
Engineering
Generation mechanism
Hypersphere constraint
Hyperspheres
Imbalanced classification
Intelligent systems
Interpolation
Machine learning
Methods
Noise
Optimization
Original Article
Oversampling
SMOTE
Straight lines
Synthetic oversampling
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Summary:Mitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE is a well-known method to tackle this task by modifying class distribution and generating synthetic instances. However, most of the SMOTE-based methods focus on the phase of data selection, while few consider the phase of data generation. This paper proposes a hypersphere-constrained generation mechanism (HS-Gen) to improve synthetic minority oversampling. Unlike linear interpolation commonly used in SMOTE-based methods, HS-Gen generates a minority instance in a hypersphere rather than on a straight line. This mechanism expands the distribution range of minority instances with significant randomness and diversity. Furthermore, HS-Gen is attached with a noise prevention strategy that adaptively shrinks the hypersphere by determining whether new instances fall into the majority class region. HS-Gen can be regarded as an oversampling optimization mechanism and flexibly embedded into the SMOTE-based methods. We conduct comparative experiments by embedding HS-Gen into the original SMOTE, Borderline-SMOTE, ADASYN, k -means SMOTE, and RSMOTE. Experimental results show that the embedded versions can generate higher quality synthetic instances than the original ones. Moreover, on these oversampled datasets, the conventional classifiers (C4.5 and Adaboost) obtain significant performance improvement in terms of F 1 measure and G -mean.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-022-00938-9