NDLI: Feature Selection for Cancer Classification on Microarray Expression Data

Content Provider	IEEE Xplore Digital Library
Author	Hui-Huang Hsu Ming-Da Lu
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Tamkang Univ., Taipei (Hui-Huang Hsu; Ming-Da Lu)
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.
Starting Page	153
Ending Page	158
File Size	342358
Page Count	6
File Format	PDF
ISBN	9780769533827
DOI	10.1109/ISDA.2008.198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-26
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Support Vector Machine Data mining Gene expression Microarray Diseases Support vector machines Feature Selection Pearson Correlation Coefficient Filters Cancer Classification Support vector machine classification Kernel Bioinformatics Cancer
Content Type	Text
Resource Type	Article

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