Cluster Ensemble Kernel for Kernel-based Classification

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1 Citation (Scopus)

Abstract

This paper presents a method for some semi-supervised and supervised classification problems based on properties of the averaged co-Association matrix obtained with a cluster ensemble. The ensemble clustering is performed as a preliminary step of data processing. The main property states that the matrix is a valid kernel matrix, thus it can be used in different classification methods that use kernels such as Kernel Nearest Neighbor, SVM, Kernel Fisher Discriminant. Some properties of the suggested method connected with its convergence to optimal classifier are studied. Numerical experiments show that the accuracy of the proposed algorithms is often higher than other state-of-The-Art methods, especially under the presence of complex data structures and noise distortions.

Original languageEnglish
Title of host publicationSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages670-674
Number of pages5
ISBN (Electronic)9781728144016
DOIs
Publication statusPublished - Oct 2019
Event2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duration: 21 Oct 201927 Oct 2019

Publication series

NameSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
CountryRussian Federation
CityNovosibirsk
Period21.10.201927.10.2019

Keywords

  • cluster ensemble
  • co-Association matrix
  • kernel-based classification

OECD FOS+WOS

  • 1.01 MATHEMATICS
  • 1.02 COMPUTER AND INFORMATION SCIENCES
  • 1.03 PHYSICAL SCIENCES AND ASTRONOMY
  • 2.02 ELECTRICAL ENG, ELECTRONIC ENG
  • 5.09 OTHER SOCIAL SCIENCES

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