By Topic

Improving the Performance of FLN by Using Similarity Measures and Evolutionary Algorithms

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
$31 $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

3 Author(s)
Cripps, A. ; Finance Middle Tennessee State Univ., Murfreesboro ; Pettey, C. ; Nghiep Nguyen

In this work, we show that the underlying inclusion measure used by fuzzy lattice neurocomputing classifiers can be extended to various similarity and distance measures often used in cluster analysis. We show that for some similarity measures, we can modify the measure to weigh the contribution of each attribute found in the data set. Furthermore, we show that evolutionary algorithms such as genetic algorithms, tabu search, particle swarm optimization, and differential evolution can be used to weigh the importance of each attribute and that this weighting can provide additional improvements over simply using the similarity measure. We provide evidence that these new techniques provide significant improvements by applying them to the Cleveland heart data.

Published in:

Fuzzy Systems, 2006 IEEE International Conference on

Date of Conference:

0-0 0