Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3104-3112.DOI: 10.11772/j.issn.1001-9081.2021010062

• Artificial intelligence • Previous Articles     Next Articles

Structure-fuzzy multi-class support vector machine algorithm based on pinball loss

Kai LI, Jie LI()   

  1. School of Cyber Security and Computer,Hebei University,Baoding Hebei 071002,China
  • Received:2021-01-13 Revised:2021-03-20 Accepted:2021-04-14 Online:2021-11-20 Published:2021-11-10
  • Contact: Jie LI
  • About author:Ll Kai,born in 1963,Ph. D.,professor. His research interestsinclude machine learning, data mining
    LI Jie,born in 1996,M. S. candidate. Her research interestsinclude machine learning ,data mining.
  • Supported by:
    the Natural Science Foundation of Hebei Province(F2018201060)


李凯, 李洁()   

  1. 河北大学 网络空间安全与计算机学院,河北 保定 071002
  • 通讯作者: 李洁
  • 作者简介:李凯(1963—)男,河北保定人,教授,博士,主要研究方向:机器学习,数据挖掘
  • 基金资助:


The Multi-Class Support Vector Machine (MSVM) has the defects such as strong sensitivity to noise, instability to resampling data and lower generalization performance. In order to solve the problems, the pinball loss function, sample fuzzy membership degree and sample structural information were introduced into the Simplified Multi-Class Support Vector Machine (SimMSVM) algorithm, and a structure-fuzzy multi-class support vector machine algorithm based on pinball loss, namely Pin-SFSimMSVM, was proposed. Experimental results on synthetic datasets, UCI datasets and UCI datasets adding different proportions of noise show that, the accuracy of the proposed Pin-SFSimMSVM algorithm is increased by 0~5.25 percentage points compared with that of SimMSVM algorithm. The results also show that the proposed algorithm not only has the advantages of avoiding indivisible areas of multi-class data and fast calculation speed, but also has good insensitivity to noise and stability to resampling data. At the same time, the proposed algorithm considers the fact that different data samples play different roles in classification and the important prior knowledge contained in the data, so that the classifier training is more accurate.

Key words: multi-class, Support Vector Machine (SVM), pinball loss, structural information, fuzzy membership degree



关键词: 多分类, 支持向量机, pinball损失, 结构信息, 模糊隶属度

CLC Number: