Feature Selection for Cancer Classification on Microarray Expression Data

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Proceeding > Item 987654321/75831

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/75831

Title:	Feature Selection for Cancer Classification on Microarray Expression Data
Authors:	Hsu, Hui-huang;Lu, Ming-da
Contributors:	淡江大學資訊工程學系
Keywords:	Cancer Classification;Feature Selection;Microarray;Pearson Correlation Coefficient;Support Vector Machine
Date:	2008-11
Issue Date:	2012-04-17 22:07:19 (UTC+8)
Publisher:	IEEE; International Fuzzy Systems Association; National Kaohsiung University of Applied Sciences
Abstract:	Microarray is an important tool in gene analysis research. It can help identify genes that might cause various cancers. In this paper, we use feature selection methods and the support vector machine (SVM) to search for the disease-causing genes in microarray data of three different cancers. The feature selection methods are based on Euclidian distance (ED) and Pearson correlation coefficient(PCC). We investigated the effect on prediction results by training the SVM with different numbers of features and different kinds of kernels. The results show that linear kernel is the fittest kernel for this problem. Also, equal or higher accuracy can be achieved with only 15 to 100 features which are selected from 7129 or more features of the original data sets.
Relation:	Proceedings of the Eighth International Conference on Intelligent Systems Design and Applications (ISDA'08) v.3, pp.153-158
DOI:	10.1109/ISDA.2008.198
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

Files in This Item:

File	Description	Size	Format
Feature Selection for Cancer Classification on Microarray Expression Data.pdf		334Kb	Adobe PDF	598	View/Open
index.html		0Kb	HTML	306	View/Open