Faten Kharbat
Knowledge discovery from medical data: an empirical study with XCS
Kharbat, Faten; Odeh, Mohammed; Bull, Larry
Authors
Mohammed Odeh Mohammed.Odeh@uwe.ac.uk
Associate Professor in Software Engineering
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Contributors
Lawrence Bull Larry.Bull@uwe.ac.uk
Editor
Ester Bernado-Mansilla
Editor
John Holmes
Editor
Abstract
In this chapter we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a primary breast cancer data set. Our results indicate more effective knowledge discovery than with C4.5.
Citation
Kharbat, F., Odeh, M., & Bull, L. (2008). Knowledge discovery from medical data: an empirical study with XCS. In L. Bull, E. Bernado-Mansilla, & J. Holmes (Eds.), Learning Classifier Systems in Data Mining. Springer
Publication Date | Jan 1, 2008 |
---|---|
Peer Reviewed | Peer Reviewed |
Series Title | Studies in Computational Intelligence |
Series Number | 125 |
Book Title | Learning Classifier Systems in Data Mining |
ISBN | 9783540789789 |
Keywords | LCS, data mining, medical data sets |
Public URL | https://uwe-repository.worktribe.com/output/1019721 |
Publisher URL | http://www.springer.com/engineering/mathematical/book/978-3-540-78978-9 |
You might also like
A generalised dropout mechanism for distributed systems
(2022)
Journal Article
Nonbinary representations in the NK and NKCS models
(2022)
Journal Article
Evolving Boolean regulatory networks with variable gene expression times
(2021)
Book Chapter
On coevolution: Asymmetry in the NKCS model
(2021)
Journal Article