Title :
Multi-class feature selection using Pairwise-class and All-class techniques
Author :
Chen, Bo ; Li, Guo-Zheng ; You, Mingyu
Author_Institution :
Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China
Abstract :
Feature selection has been a key technique in massive data processing, e.g. microarray data analysis with few samples but high dimensions. One common problem in multi-class data analysis is the unbalanced recognition accuracies among classes, which leads to poor system performance. One main reason is that most feature selection methods focus on the performance of whole dataset while pay little attention to single class (especially the minority class). In this paper, a novel hybrid feature selection method with Pairwise-class and All-class techniques (namely FSPA) is proposed to remedy the problem. Strategy of round-robin is embedded into FSPA to reduce the bias among classes. Experimental results on four public microarray datasets show that FSPA helps to achieve higher classification accuracy and balance the performance among classes.
Keywords :
bioinformatics; data analysis; genomics; all-class technique; data processing; gene expression microarray; high classification accuracy; hybrid feature selection method; microarray data analysis; multiclass feature selection; pairwise-class; public microarray datasets; Feature Selection; Gene selection; Microarray; Multi-Class; component;
Conference_Titel :
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on
Conference_Location :
Hong, Kong
Print_ISBN :
978-1-4244-8303-7
Electronic_ISBN :
978-1-4244-8304-4
DOI :
10.1109/BIBMW.2010.5703878