Title :
Wavelet-Based Feature Extraction for Microarray Data Classification
Author :
Li, Shutao ; Liao, Chen ; Kwok, James T.
Author_Institution :
Hunan Univ., Changsha
Abstract :
Microarray data typically have thousands of genes, and thus feature extraction is a critical problem for accurate cancer classification. In this paper, a feature extraction method based on the discrete wavelet transform (DWT) is proposed. The approximation coefficients of DWT, together with some useful features from the high-frequency coefficients selected by the maximum modulus method, are used as features. The combined coefficients are then forwarded to a SVM classifier. Experiments are performed on two standard benchmark data sets: ALL/AML Leukemia and Colon tumor. Experimental results show that the proposed method can achieve state-of-the-art performance on cancer classification.
Keywords :
biology computing; cancer; discrete wavelet transforms; feature extraction; genetics; pattern classification; support vector machines; tumours; SVM classifier; cancer classification; discrete wavelet transform; microarray data classification; wavelet-based gene feature extraction; Bioinformatics; Cancer; DNA; Discrete wavelet transforms; Feature extraction; Filters; Neoplasms; Support vector machines; Wavelet coefficients; Wavelet transforms;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.247208