Advantages of using fuzzy class memberships in self-organizing map and support vector machines

S. Sohn, C. H. Dagli

Research output: Contribution to conferencePaper

16 Scopus citations

Abstract

Self-organizing map (SOM) is naturally unsupervised learning, but if a class label is known, it can be used as the classifier. In SOM classifier, each neuron is assigned a class label based on the maximum class frequency and classified by a nearest neighbor strategy. The drawback when using this strategy is that each pattern is treated by equal importance in counting class frequency regardless of its typicalness. For this reason, the fuzzy class membership can be used instead of crisp class frequency and this fuzzy-membership-label neuron provides another perspective of a feature map. This fuzzy class membership can be also used to select training samples in support vector machines (SVM) classifier. This method allows us to reduce the training set as well as support vectors without significant loss of classification performance.

Original languageEnglish (US)
Pages1886-1890
Number of pages5
StatePublished - Jan 1 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period7/15/017/19/01

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Sohn, S., & Dagli, C. H. (2001). Advantages of using fuzzy class memberships in self-organizing map and support vector machines. 1886-1890. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.