Title :
Output-Coding and SVM for Multiclass Microarray Classification
Author :
Shen, L. ; Tan, E.C.
Abstract :
Multiclass cancer classification based on microarray data has been studied in this paper. A generalized output coding scheme combined with support vector machines as binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. By using the random coding strategy and recursive feature elimination, the testing accuracy that we have achieved is as high as 80.4% on the GCM data which has 14 classes. Comparing with the other classification methods, our method has shown its superiority in classificatory performance.
Keywords :
cancer; encoding; feature extraction; learning (artificial intelligence); medical diagnostic computing; pattern classification; random processes; recursive estimation; support vector machines; SVM; binary classifiers; feature selection methods; generalized output coding scheme; multiclass cancer classification; multiclass microarray classification; random coding strategy; recursive feature elimination; support vector machines; Cancer; Decoding; Diversity reception; Error correction codes; Learning systems; Machine learning; Matrix decomposition; Support vector machine classification; Support vector machines; Testing;
Conference_Titel :
TENCON 2005 2005 IEEE Region 10
Conference_Location :
Melbourne, Qld.
Print_ISBN :
0-7803-9311-2
Electronic_ISBN :
0-7803-9312-0
DOI :
10.1109/TENCON.2005.301161
