DocumentCode :
478563
Title :
Artificial Immune Recognition System for DNA Microarray Data Analysis
Author :
Chen, Chuanliang ; Xu, Chuan ; Bie, Rongfang ; Gao, X.Z.
Author_Institution :
Coll. of Inf. Sci. & Technol, Beijing Normal Univ., Beijing
Volume :
6
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
633
Lastpage :
637
Abstract :
Artificial immune systems (AIS) are emerging information processing methods, which embody the principles of biological immune systems for tackling complex realworld problems. The artificial immune recognition system (AIRS) is a new kind of supervised learning AIS. The development of microarray technology has supplied a large volume of data for the prediction and diagnosis of cancer. Many popular machine learning techniques have been used in the microarray data analysis. In this paper, we apply AIRS to perform the microarray data classification based on an improved version of the information gain feature selection method. Three traditional classifiers have also been employed in our experiments for performance comparison. The results demonstrate the promising ability of AIRS in the microarray data analysis.
Keywords :
artificial immune systems; cancer; data analysis; feature extraction; lab-on-a-chip; learning (artificial intelligence); pattern classification; DNA microarray data analysis; artificial immune recognition system; biological immune systems; cancer diagnosis; feature selection method; machine learning; microarray data classification; supervised learning; Artificial immune systems; Biology computing; Cancer; DNA computing; Data analysis; Entropy; Immune system; Information processing; Information science; Performance gain; Artificial Immune Recognition System; Artificial Immune Systems; DNA Microarray Data Analysis; Natural Computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.145
Filename :
4667912
Link To Document :
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