By Topic

Particle Swarm Optimization for classification of breast cancer data using single and multisurface methods of data separation

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

2 Author(s)
Tewolde, G.S. ; Kettering Univ., Flint ; Hanna, D.M.

This paper exploits the simplicity, efficiency and flexibility of the particle swarm optimization (PSO) method to propose a single and multisurface based data separation methods for classification of Breast Cancer Data. Like most artificial intelligence based techniques the first step of the proposed approaches involve the training of the PSO-based classifiers according to pre-defined data separation methods, using part of the dataset for training. The performances of the classifiers are then tested on the remaining dataset to measure the classification accuracy. The training and testing datasets are derived from the Breast Cancer database obtained from the UCI machine learning repository. Both separation methods produce good classification performance; however, the method based on multiple separating surfaces achieves the best result of 100% classification accuracy on both the training and testing datasets.

Published in:

Electro/Information Technology, 2007 IEEE International Conference on

Date of Conference:

17-20 May 2007