Title :
Adaptive multi-class support vector machine for microarray classification and gene selection
Author :
Li, Juntao ; Jia, Yingmin ; Du, Junping ; Yu, Fashan
Author_Institution :
Dept. of Syst. & Control, Beihang Univ. (BUAA), Beijing, China
Abstract :
This paper proposes an adaptive multi-class support vector machine for simultaneous microarray classification and gene selection. By evaluating the gene ranking significance, the adaptive multi-class support vector machine is shown to encourage an adaptive grouping effect in the process of building classifiers, thus leading a sparse multi-classifiers with enhanced interpretability. Based on a reasonable correlation between the two regularization parameters, an efficient solution path algorithm is developed for solving the proposed support vector machine. Experiments performed on the leukaemia data set are provided to verify the obtained results.
Keywords :
bioinformatics; genetics; lab-on-a-chip; learning (artificial intelligence); pattern classification; support vector machines; adaptive grouping effect; adaptive multiclass support vector machine; gene ranking significance; gene selection; leukaemia data set; machine learning; microarray classification; reasonable correlation; regularization parameter; solution path algorithm; Adaptive control; Automatic control; Control systems; Electronic mail; Gene expression; Learning systems; Programmable control; Support vector machine classification; Support vector machines; Telecommunication control; Gene selection; microarray classification; multi-class support vector machine (MSVM); solution path;
Conference_Titel :
ICCAS-SICE, 2009
Conference_Location :
Fukuoka
Print_ISBN :
978-4-907764-34-0
Electronic_ISBN :
978-4-907764-33-3