skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

A soft-margin convex polyhedron classifier for nonlinear task with noise tolerance

Applied intelligence (Dordrecht, Netherlands), 2021, Vol.51 (1), p.453-466 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;Springer Science+Business Media, LLC, part of Springer Nature 2020. ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-020-01854-6

Full text available

Citations Cited by
  • Title:
    A soft-margin convex polyhedron classifier for nonlinear task with noise tolerance
  • Author: Leng, Qiangkui ; He, Zuowei ; Liu, Yuqing ; Qin, Yuping ; Li, Yujian
  • Subjects: Artificial Intelligence ; Classification ; Classifiers ; Computer Science ; Datasets ; Hyperplanes ; Machines ; Manufacturing ; Mechanical Engineering ; Noise ; Noise tolerance ; Polyhedra ; Processes ; Radial basis function ; Slack variables ; Support vector machines
  • Is Part Of: Applied intelligence (Dordrecht, Netherlands), 2021, Vol.51 (1), p.453-466
  • Description: As a special form of piecewise linear classifier, the convex polyhedron classifier is simple to implement and achieves rapid response in real-time classification. However, it usually performs badly in the case of high noise where severe boundary intrusion exists. Inspired by the scheme of soft margin in support vector machine, in this paper we propose a soft-margin convex polyhedron classifier for nonlinear classification task. The base (linear) classifier is first generalized to its soft-margin version through kernelization process and slack variables. In each local region, the soft-margin base classifier learns a decision hyperplane with noise tolerance. Then, a series of learned hyperplanes are structurally integrated into a convex polyhedron classifier, which is essentially a convex polyhedron that encloses one class and excludes the other class outside. Experimental results on fifteen benchmark datasets show the proposed soft-margin convex polyhedron classifier is comparable to linear support vector machine and four piecewise linear classifiers, but does not perform as well as the support vector machine with radial basis function kernel in general. When random noises are added to datasets, the soft-margin convex polyhedron classifier achieves similar or better accuracies with the well-known classifiers used for comparison, implying its promising ability of noise tolerance.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0924-669X
    EISSN: 1573-7497
    DOI: 10.1007/s10489-020-01854-6
  • Source: ProQuest One Psychology
    AUTh Library subscriptions: ProQuest Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait