Advantages of using Fuzzy Class Memberships in Self-organizing Map and Support Vector Machines

Author

S. Sohn
Cihan H. Dagli, Missouri University of Science and TechnologyFollow

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.

Recommended Citation

S. Sohn and C. H. Dagli, "Advantages of using Fuzzy Class Memberships in Self-organizing Map and Support Vector Machines," Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1886 - 1890, Institute of Electrical and Electronics Engineers, Jan 2001.

Department(s)

Engineering Management and Systems Engineering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2001