Title :
A cancer classification method based on association rules
Author :
Wang, MeiHua ; Su, XiongBin ; Liu, FuMing ; Cai, Ruichu
Author_Institution :
Coll. of Inf., South China Agric. Univ., Guangzhou, China
Abstract :
Gene expression data based cancer classification is of great importance to the computer aided diagnosis. In this paper, we propose a novel cancer selection method, AR-SVM. In AR-SVM, association rules are used as feature extraction approach to catch the non-linear relation among different genes, and support vector machine is used to classify the transformed gene expression data. The proposed method achieves both high classification accuracy and good biological interpretability. The experimental results on various gene expression datasets show that AR-SVM achieves the highest classification accuracy in comparison with existing gene expression classification methods.
Keywords :
bioinformatics; cancer; data mining; feature extraction; genetics; medical diagnostic computing; patient diagnosis; pattern classification; support vector machines; AR-SVM; association rules; biological interpretability; cancer classification method; cancer selection method; classification accuracy; computer aided diagnosis; feature extraction; nonlinear relation; support vector machine; transformed gene expression data classification; Accuracy; Association rules; Cancer; Gene expression; Support vector machines; Training; association rules; computer aided diagnosis; gene expression data; support vector machine;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
DOI :
10.1109/FSKD.2012.6234193