[1]ZHAO Shuaiqun,GUO Husheng,WANG Wenjian.Granular support vector machine with bidirectional control of division-fusion[J].CAAI Transactions on Intelligent Systems,2019,14(6):1243-1254.[doi:10.11992/tis.201904047]
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Granular support vector machine with bidirectional control of division-fusion

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1243-1254 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Granular support vector machine with bidirectional control of division-fusion
Author(s):
ZHAO Shuaiqun1;  GUO Husheng1; 2;  WANG Wenjian2
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2. Key Laboratory of Computational Intelligence and Chinese Information Processing, Shanxi University, Taiyuan 030006, China
Keywords:
support vector machine (SVM); granular support vector machine (GSVM); division; fusion; strong information granule; weak information granule; dynamic mechanism; bidirectional control
CLC:
TP18
DOI:
10.11992/tis.201904047
Abstract:
Granular support vector machine (GSVM) introduces the method of granular computing to divide the original dataset; therefore, GSVM improves the efficiency of the support vector machine (SVM). The traditional GSVM adopts the static granules partitioning mechanism to extract representative information from the divided data clusters for model training, which can effectively increase the learning efficiency of the SVM. However, the GSVM uses the same processing way for different information granules, which may lead to a decline in the generalization ability because of two reasons: (i) No sufficient valid information is extracted from the strong information granules that are close to the hyper-plane, and (ii) excess of the weak information of granules far from the hyper-plane is reserved. These all reduce the learning performance of the SVM. To address this problem, this study proposes a division and fusion SVM model based on dynamical granulation, namely DFSVM. With the DFSVM, the information from the strong information granules near the hyper-plane is divided in depth, and weak information from weak information granules far from the hyper-plane is selectively merged to dynamically maintain the stability of the size of the training samples. The experiments demonstrate that this model can significantly improve the SVM learning efficiency, ensuring the training precision of the model.
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