基于迹比准则与+L-R方法的特征选择算法

Feature Selection Algorithm Based on Trace Ratio Criterion and +L-R Method

提出一种将迹比准则和基于错分区域的+L-R方法相结合的特征选择算法。该算法使用迹比算法得到优秀特征子集,对分类产生的错分区域进行+L-R选择得到新特征,新特征可以区分之前被错分的数据,从而降低错分率。采用+L-R算法降低数据冗余。实验结果表明,该算法有效改进迹比准则特征选择算法,同时降低错分率。

A new ＋L-R feature selection algorithm is proposed which combines trace ratio criterion selection and ＋L-R method based on error region,it uses trace ratio selection to obtain a optimal subset and uses error region by ＋L-R selection to get a new feature which can classify error sample efficiently in the region of error samples and error classification rate can be decreased efficiently.Using ＋L-R algorithm can reduce data redundancy.Experimental results show that the proposed algorithm improves trace ratio criterion significantly and lower error rate can be achieved at the same time.