Fuzzy k-NN SVM

Hui Chuan Cheng, Chan Yun Yang, Gene Eu Jan, Angela Shin Yih Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A fuzzy support vector machine emphasized the noise contamination locality in its first filtering stage is proposed. As a consequence to assign locally fuzzy memberships to the learning samples in the preprocessing filtering stage, the locality enhances the support vector machine, which is originally devised to learn a classifier with the global quadratic optimization, to compromisingly adapt to the individual attitude of the learning data. The paper employed a fuzzy k-NN rule as the preprocessor. The k-NN approach is advantageous to with its nonparametric nature, learning directly from the given prototypes without additional complex computation, is really appropriate for the local-global combination. By unraveling the individual attitude in the contaminated mess of the dataset as a fuzzy membership, an underlying fuzzy support vector machine is thus applied to finish the model. The model, originated as a variety of the fuzzy support vector machine, not only shares the merits of its crucial robustness which inspired by the global optimization, but also exhibits its capability in keeping the room for the learning samples in their representation of local confidence.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1227-1232
Number of pages6
ISBN (Electronic)9781479986965
DOIs
StatePublished - 12 01 2016
Externally publishedYes
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 09 10 201512 10 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/TerritoryHong Kong
CityKowloon Tong
Period09/10/1512/10/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • fuzzy support vector machines
  • knearest neighbor
  • local-global decomposition
  • robust classifier

Fingerprint

Dive into the research topics of 'Fuzzy k-NN SVM'. Together they form a unique fingerprint.

Cite this