Abstract
Classification for medical diagnosis is an important problem in the field of pattern recognition. We introduce a new method for classification based on repeated analysis of information tailored to small data sets - the Rote Learning Classifier System. using the Wisconsin Breast Cancer study, this method was compared to three other methods of classification: Mahalanobis-Taguchi Systems, Finite State Classifiers, and Neural Networks. It was found that for the given data set, the Rote Learning Classifier System outperformed the other methods of classification. This new algorithm correctly classified over 92% of the data set. © 2012 IEEE.
Recommended Citation
B. Daniels et al., "Introduction of R-LCS and Comparative Analysis with FSC and Mahalanobis-Taguchi Method for Breast Cancer Classification," 2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012, pp. 283 - 289, article no. 6217242, Institute of Electrical and Electronics Engineers, Jul 2012.
The definitive version is available at https://doi.org/10.1109/CIBCB.2012.6217242
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-146731189-2
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
25 Jul 2012
