A GA-Based Feature Extraction and Its Application

Yu Zhefu, Huibiao Lu, Chuanying Jia
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引用次数: 2

Abstract

In order to obtain an explicit and non-linear regress function, a new feature extraction was presented on the basis of linear support victor regression and genetic algorithm. Firstly, the linear input space  in training data was mapped to a polynomial space, which can solve non-linear regression questions without complex and vague kernel skills. Then, a genetic algorithm was used to extract features from high dimension polynomial space.  Suitable fitness function guaranteed that the extracted features had the biggest influence on the output in training data. Finally, linear support victor regression was introduced to the extracted features. An explicit non-linear regress function can be find. An application showed the efficiency of the new feature extraction.
基于遗传算法的特征提取及其应用
为了获得显式的非线性回归函数,提出了一种基于线性支持胜利者回归和遗传算法的特征提取方法。首先,将训练数据中的线性输入空间映射到多项式空间,可以解决非线性回归问题,而不需要复杂和模糊的核技巧。然后,利用遗传算法从高维多项式空间中提取特征;合适的适应度函数保证了提取的特征对训练数据输出的影响最大。最后,对提取的特征引入线性支持胜利者回归。可以找到一个显式的非线性回归函数。应用表明了新特征提取方法的有效性。
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