模式识别中基于Boosting的特征筛选

Character Choosing Based on Boosting in Pattern Recognition

提出了一种基于Boosting的特征筛选算法.根据Boosting分类训练时的训练错误率、训练过程中错误率的收敛速度以及测试错误率确定特征影响因子;利用这些影响因子对待识别目标的特征进行排序,去除冗余特征,以降低特征空间的维数.对于筛选后保留的特征,根据其影响因子进行加权,以提高目标识别的准确率.用该方法可避免其它分类学习器训练时的过学习现象,生成的分类器模型小,识别速度快,适用于对特征不易确定的目标识别.

A character choosing method is proposed based on the Boosting algorithm. The effect of the character (EC) depends on the train error, convergence speed and the test error. The character can be ordered by the effect of the character, and the redundancy character is deleted. Character choosing can reduce the dimension of the character space. The residual character is weighted by the effect of the character, and the exactness of object recognition is improved. The method will not lead to overfit like in the case of other classification learning machines, and the model of the classification is small, and is suitable for object recognition where the character is not obviously determined.