类间散度特征选择法及其应用
A Method of Between-Class Divergence Feature Selection and its Application
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摘要: 本文介绍了统计模式识别中特征选择的一种方法--类间散度法.它是以两类细胞样本 在n维特征空间中Fisher映射点集的类间差与类内差之比作为特征选择的判据.用这种方 法对五类白血球进行了特征选择,并对所选特征用训练集和测试集进行了识别效果检验,同 时,得到了特征选择的判据D与识别率之间的关系曲线.Abstract: In this paper, a method of feature selection in statistical pattern recognition the between-class divergence algorithm is introduced. It takes as its criterion of feature selection the ratio of between-class difference to within-class difference of the point set obtained from. Fisher's mapping of two classes of cell smaple in n-dimentional feature space. The method is applied to feature selection for five classes of leucocytes, and the recognition effectiveness of the selected features is examined with train set and test set. The relation between feature selection criterion D and The correct recognition tale is obtained.
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