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| Feature Extraction Base onWavelet Modulus Maxima for Microarray Data |
| Chen Xiaomei |
| Fujian Agriculture and Forestry University |
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Abstract A new method of microarray data to extract features based on wavelet modulus maxima is proposed in this paper.
First of all, the Bhattacharyya distance distributions of two classes are derived, preliminarily extracting feature genes. Then wavelet
decomposition is adopted to detect the gene mutation of high frequency coefficient, and to approximate the original signal characterization
based on low frequency. Finally the features are extracted by theoretical analysis and SVM classification, which selects the
wavelet basis and scale based on multiple experiments. The proposed method is applied on the data set (1999 Golub used in ALL
and AML). Five feature genes are extracted, whose classification test accuracy rate can reach 94.12%. It can be seen that the algorithm
has high feasibility and effectiveness, and can provide some reference for the study of the differentially expressed genes between
tumors.
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Published: 13 November 2017
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