An complementarity based feature selection method for pattern recognition

机译：基于互补性的模式识别特征选择方法

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摘要

For the pattern recognition problem, this paper proposes a feature selection method based on complementarity analysis. Analyse the separability of single feature, search the feature combination with the smallest probability of mixing region and in the mixed region with the greatest separability to reduce the probability of classification error. Compared with other feature selection algorithms, data testing result shows that the feature selection method based on complementarity analysis has a lower error recognition rate than other methods, which has verified the superiority and the advanced nature of the method.

机译：针对模式识别问题，提出一种基于互补性分析的特征选择方法。分析单个特征的可分离性，在混合区域中寻找概率最小的特征组合，在最大分离性的混合区域中寻找特征组合，以减少分类错误的可能性。与其他特征选择算法相比，数据测试结果表明，基于互补性分析的特征选择方法的错误识别率比其他方法低，证明了该方法的优越性和先进性。

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来源
《2013 3rd International Conference on Consumer Electronics, Communications and Networks》|2013年|93-96|共4页
会议地点 Xianning(CN)
作者
Wu Xinghua; Sun Yacan;
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作者单位

School of Electrical Electronic Engineering, Shandong University of Technology, Zibo, Chinac;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
complementarity analyze; feature selection; pattern recognition;

机译：互补分析;特征选择;模式识别;;

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An complementarity based feature selection method for pattern recognition

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