Reducing multiclass cancer classification to binary by output coding and SVM.
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
Shen
Li
L
School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore. shenli@pmail.ntu.edu.sg
Tan
Eng Chong
EC
D001185:Artificial Intelligence; D019992:Databases as Topic; D003658:Decision Making, Computer-Assisted; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods