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- W2382484612 abstract "Recognition model of colon cancer tumor Gene Expression Data was established by using wavelet noise reduction and support vector machine(SVM).The wavelet decomposition was made based on the test data and the method of Cross-validation was used to calculate the average classification accuracy rate of test sample in order to determine the wavelet function and wavelet decomposition level;The Energy threshold method was introduced to process wavelet coefficients for achieving noise reduction purposes;The combination method of contribution rate of gene classification and Principal component analysis(PCA) was proposed to extract characteristics of colon cancer sample data;According to the powerful nonlinear mapping ability of support vector machine,colon cancer sample data was nonlinear classified.For weakening the impact of dividing sample set on classification accuracy,Prediction accuracy of the sample set was calculated by jackknife test.The classification accuracy rate is 96.77%.Experimental results show the effectiveness of the method.The research method in this paper will be valuable to research the identification of colon cancer." @default.
- W2382484612 created "2016-06-24" @default.
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- W2382484612 date "2012-01-01" @default.
- W2382484612 modified "2023-09-27" @default.
- W2382484612 title "Wavelet De-Noising and Identification of Tumor of Gene Expression Dada" @default.
- W2382484612 hasPublicationYear "2012" @default.
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