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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/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:[資訊工程學系暨研究所] 會議論文

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