| 基于小波低频系数基因芯片数据的特征提取 |
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| 引用本文： | 刘玉杰,刘毅慧.基于小波低频系数基因芯片数据的特征提取[J].生物信息学,2011,9(3):255-258,262. |
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| 作者姓名： | 刘玉杰 刘毅慧 |
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| 作者单位： | 山东轻工业学院信息科学与技术学院智能信息处理研究所,山东,济南,250353 |
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| 摘 要： | 特征提取和分类是模式识别中的关键问题。结合小波分析理论和支持向量机理论,构造分类器模型,将前列腺癌基因芯片数据分成癌症和正常两种。提取小波低频系数表征原始数据并送入支持向量机分类器分类,实验证明:提取db1小波4层分解下的低频系数,送入分类器分类后正确分类率达到93.53%。Haar小波的正确率是92.94%。可见提取不同小波低频系数,得到的分类效果相差不大。
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| 关 键 词： | 小波分析 支持向量机 前列腺癌基因芯片数据 交叉验证 小波低频系数 |
Wavelet low-frequency Coefficients for Feature Extraction of Gene Microarray data |
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| LIU Yu-jie,LIU Yi-hui.Wavelet low-frequency Coefficients for Feature Extraction of Gene Microarray data[J].China Journal of Bioinformation,2011,9(3):255-258,262. |
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| Authors: | LIU Yu-jie LIU Yi-hui |
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| Institution: | LIU Yu-jie,LIU Yi-hui*(Institute of Intelligence Information Processing,School of Information Science and Technology,Shandong Institute of Light Industry,Jinan 250353,China) |
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| Abstract: | Feature extraction and classification are the key issues in the pattern recognition field.In the paper,we use the wavelet analysis theory and the support vector machine theory to build a classifier which can distinguish cancer tissue from prostate cancer gene microarray data.We extract wavelet low-frequency coefficients to characterize the features of prostate cancer gene microarray data,then feed the coefficients to the classifier.In these experiments,we extract db1 wavelet low-frequency coefficients at le... |
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| Keywords: | wavelet analysis support vector machine prostate cancer gene microarray data cross-validation low-frequency coefficients |
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