By Topic

Unsupervised feature selection using a fuzzy-genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
F. C. -H. Rhee ; Dept. of Electron. Eng., Hanyang Univ., Ansan, South Korea ; Young Je Lee

Presents an unsupervised feature selection method using a fuzzy-genetic approach. The method minimizes a feature evaluation index which incorporates a weighted distance used to rank the importance of the individual features. In addition, a fuzzy membership function is employed to determine the degree of closeness for each pair of patterns which are used in the feature evaluation index. A genetic algorithm is then applied to find an optimal set of weighting coefficients that minimizes the evaluation index. The final weighting coefficients denote the importance of each feature. Several experimental results are given.

Published in:

Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE '99. 1999 IEEE International  (Volume:3 )

Date of Conference:

22-25 Aug. 1999