| 基于支持向量机的特征选择 |
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| 引用本文： | 张先荣,范丽亚.基于支持向量机的特征选择[J].聊城大学学报(自然科学版),2011,24(2):18-22,27. |
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| 作者姓名： | 张先荣 范丽亚 |
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| 作者单位： | 聊城大学数学科学学院,山东聊城,252059 |
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| 基金项目： | 国家自然科学基金资助项目，山东省自然科学基金资助项目 |
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| 摘 要： | 主要研究了基于支持向量机的特征选择方法——特征权法，通过对两组数据进行试验，说明了特征权法在分类效果上优于F-得分法和支持向量机．
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| 关 键 词： | 特征选择 特征权法 F-得分法 支持向量机 错分率 |
Feature Selection Based on Support Vector Machine |
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| GE Min-min FAN Li-ya.Feature Selection Based on Support Vector Machine[J].Journal of Liaocheng University:Natural Science Edition,2011,24(2):18-22,27. |
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| Authors: | GE Min-min FAN Li-ya |
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| Institution: | GE Min-min FAN Li-ya (School of Mathematical Sciences,Liaocheng University,Liaocheng 252059,China) |
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| Abstract: | This paper is devoted to study a feature election method based on support vector machine feature weight. Experiments with two kinds of data taken from UCI machine learning repository show that feature weight method is superior to F-score method and SVM on |
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| Keywords: | feature selection feature weight method F-score method support vector machines misclassification rate |
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