by Heo, G, Klette, R, Woo, YW, Kim, K-B and Kim, NH
Abstract:
The support vector machine (SVM) of statistical learning theory was successfully applied in various fields, but still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To relax this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. The FSVM also has been further extended in two ways, by adopting a different objective function with the help of domain-specific knowledge, or by employing a different membership calculation method. In this paper we follow the second approach by proposing a new membership calculation method using a fuzzy k nearest neighbor classifier (F-KNN). Although there are already several membership calculation methods to enhance the performance of the FSVM, one problem in those methods is that they assume a specific data distribution. The F-KNN does not assume any data distribution, which helps the proposed method to accommodate various data distributions in real world problems. The proposed algorithm was applied to an insect footprint classification problem, and results verify the effectiveness of the method. © 2010 IEEE.
Reference:
Fuzzy support vector machine with a fuzzy nearest neighbor classifier for insect footprint classification (Heo, G, Klette, R, Woo, YW, Kim, K-B and Kim, NH), In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, 2010.
Bibtex Entry:
@inproceedings{heo2010fuzzyclassification,
author = "Heo, G and Klette, R and Woo, YW and Kim, K-B and Kim, NH",
booktitle = "2010 IEEE World Congress on Computational Intelligence, WCCI 2010",
title = "Fuzzy support vector machine with a fuzzy nearest neighbor classifier for insect footprint classification",
year = "2010",
abstract = "The support vector machine (SVM) of statistical learning theory was successfully applied in various fields, but still suffers from noise sensitivity originating from the fact that all the data points are treated equally. To relax this problem, the SVM was extended into a fuzzy SVM (FSVM) by the introduction of fuzzy memberships. The FSVM also has been further extended in two ways, by adopting a different objective function with the help of domain-specific knowledge, or by employing a different membership calculation method. In this paper we follow the second approach by proposing a new membership calculation method using a fuzzy k nearest neighbor classifier (F-KNN). Although there are already several membership calculation methods to enhance the performance of the FSVM, one problem in those methods is that they assume a specific data distribution. The F-KNN does not assume any data distribution, which helps the proposed method to accommodate various data distributions in real world problems. The proposed algorithm was applied to an insect footprint classification problem, and results verify the effectiveness of the method. © 2010 IEEE.",
doi = "10.1109/FUZZY.2010.5584598",
isbn = "9781424469208",
language = "eng",
}