Brief communication: Reducing multiclass cancer classification to binary by output coding and SVM

Authors:
Li Shen;Eng Chong Tan
Affiliations:
School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore;School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore
Venue:
Computational Biology and Chemistry
Year:
2006

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Abstract

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.