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

Chunlin Gong, Liangxian Gu

科研成果: 期刊稿件文章同行评审

18 引用 (Scopus)

摘要

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.

源语言英语
文章编号5685970
期刊Mathematical Problems in Engineering
2016
DOI
出版状态已出版 - 2016

指纹

探究 'A Novel SMOTE-Based Classification Approach to Online Data Imbalance Problem' 的科研主题。它们共同构成独一无二的指纹。

引用此