Feature extraction and classification of tumor based on wavelet package and support vector machines

Authors:
Shulin Wang;Ji Wang;Huowang Chen;Shutao Li
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, Hunan, China and School of Computer and Communication, Hunan University, Changsha, Hunan, China;School of Computer Science, National University of Defense Technology, Changsha, Hunan, China;School of Computer Science, National University of Defense Technology, Changsha, Hunan, China;College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

DNA microarray experiments provide us with huge amount of gene expression data, which leads to a dimensional disaster for extracting features related to tumor. A wavelet package decomposition based feature extraction method for tumor classification was proposed, by which eigenvectors are extracted from gene expression profiles and used as the input of support vector machines classifier. Two well-known datasets are examined using the novel feature extraction method and support vector machines. Experiment results show that the 4-fold cross-validated accuracy of 100% is obtained for the leukemia dataset and 93.55% for the colon dataset.