By Topic

Evolving Neural Network Classifiers and Feature Subset Using Artificial Fish Swarm

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

5 Author(s)
Meifeng Zhang ; Research Centre of Information and Control, Dalian University of Technology, Dalian, Liaoning Province, China; Zhengzhou Institute of Light Industry, Zhengzhou, Henan Province, China. mfzhang@dlut.edu.cn, mfzhang@zzuli.edu.cn ; Cheng Shao ; Fuchao Li ; Yong Gan
more authors

As a novel simulated evolutionary computation technique, artificial fish swarm algorithm (AFSA) shows many promising characters. This paper presents the use of AFSA as a new tool which sets up a neural network (NN), adjusts its parameters, and performs feature reduction, all simultaneously. In the optimization process, all features and hidden units are encoded into a real-valued artificial fish (AF), and give out the method of designing fitness function. The experimental results on several public domain data sets from UCI show that our algorithm can obtain an optimal NN with fewer input features and hidden units, and perform almost as good as even better than an original complex NN with entire input features. And also indicate that optimizing a network classifier for a specific task has the potential to produce a simple classifier with low classification error and good generalization ability

Published in:

2006 International Conference on Mechatronics and Automation

Date of Conference:

25-28 June 2006