A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem

In many practical engineering applications, data are usually collected in online pattern. However, if the classes of these data are severely imbalanced, the classification performance will be restricted. In this paper, a novel classification approach is proposed to solve the online data imbalance pr...

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Bibliographic Details
Published in:Mathematical problems in engineering 2016-01, Vol.2016 (2016), p.1-14
Main Authors: Gong, Chunlin, Gu, Liangxian
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Classification
Data processing
Datasets
Distance learning
Entropy (Information theory)
Fault diagnosis
Fuzzy sets
Granular materials
Granulation
Granules
Lower bounds
Machine learning
Mathematical models
Mathematical problems
Neural networks
Numerical stability
Online
Oversampling
Research funding
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Summary:In many practical engineering applications, data are usually collected in online pattern. However, if the classes of these data are severely imbalanced, the classification performance will be restricted. In this paper, a novel classification approach is proposed to solve the online data imbalance problem by integrating a fast and efficient learning algorithm, that is, Extreme Learning Machine (ELM), and a typical sampling strategy, that is, the synthetic minority oversampling technique (SMOTE). To reduce the severe imbalance, the granulation division for major-class samples is made according to the samples’ distribution characteristic, and the original samples are replaced by the obtained granule core to prepare a balanced sample set. In online stage, we firstly make granulation division for minor-class and then conduct oversampling using SMOTE in the region around granule core and granule border. Therefore, the training sample set is gradually balanced and the online ELM model is dynamically updated. We also theoretically introduce fuzzy information entropy to prove that the proposed approach has the lower bound of model reliability after undersampling. Numerical experiments are conducted on two different kinds of datasets, and the results demonstrate that the proposed approach outperforms some state-of-the-art methods in terms of the generalization performance and numerical stability.
ISSN:1024-123X
1563-5147
DOI:10.1155/2016/5685970