Title :
Dimension Reduction Based on Modified Maximum Margin Criterion for Tumor Classification
Author :
Zhang, Shanwen ; Jing, Rongzhi
Author_Institution :
Sias Int. Univ., Zhengzhou, China
Abstract :
Based on Maximum margin criterion (MMC), a new algorithm, named modified MMC, is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and aims at maximizing the average margin between classes in the projected space. After projecting, the considered pair wise points within the same class are as close as possible, while those between different classes are as far as possible. The performance on two gene expression profiles datasets demonstrates the effectiveness of the proposed method.
Keywords :
gene therapy; genetics; tumours; average margin; gene expression profile dataset; linear transformation; modified maximum margin criterion; pairwise points; supervised dimensionality reduction; tumor classification; Cancer; Classification algorithms; DNA; Gene expression; Principal component analysis; Support vector machine classification; Tumors; Gene expression profiles; Maximum margin criterion (MMC); Modified MMC; Tumor classification;
Conference_Titel :
Information and Computing (ICIC), 2011 Fourth International Conference on
Conference_Location :
Phuket Island
Print_ISBN :
978-1-61284-688-0
DOI :
10.1109/ICIC.2011.148