A Segmented Local Offset Method for Imbalanced Data Classification Using Quasi-Linear Support Vector Machine

Peifeng Liang, Xin Yuan, Weite Li, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Within-class imbalance problems often occur in imbalance classification which worsen the imbalance distribution problem and increase the learning concept complexity. However, most of existing methods for imbalanced classification focus on rectifying the between-class which are insufficiencies and inappropriateness in many different scenarios. This paper proposes a novel quasi-linear SVM with local offset adjustment method for imbalance classification problem. Our chief aim is to use leaning offsets of sub-clusters obtained according to imbalance ratios of sub-clusters to adjust classifier to achieve the best results. For this purpose, firstly, a geometry-based partitions method for imbalance dataset is introduced to partition the input space into several linearly separable partitions so as to construct a quasi-linear kernel and obtain an SVM classifier. Then a local offset method based on F-score value for linearly separable imbalance dataset is introduced to obtain leaning offset of each partition. At last the quasi-linear SVM with local offset adjustment is used to get the classifier for imbalance datasets. Simulation results on different real different real world datasets show that the proposed method is effective for imbalanced data classifications.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages746-751
Number of pages6
Volume2018-August
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 2018 Nov 26
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 2018 Aug 202018 Aug 24

Other

Other24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period18/8/2018/8/24

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Liang, P., Yuan, X., Li, W., & Furuzuki, T. (2018). A Segmented Local Offset Method for Imbalanced Data Classification Using Quasi-Linear Support Vector Machine. In 2018 24th International Conference on Pattern Recognition, ICPR 2018 (Vol. 2018-August, pp. 746-751). [8545796] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8545796