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包裹式选择
特征选择的一类方法。包裹式特征选择方法把最终要使用的学习器的性能作为特征子集的评价准则,其目的是为给定学习器选择有利于其性能、“量身定做” 的特征子集,一般而言,由于包裹式特征选择方法直接针对学习器进行优化,因此从最终学习性能上看,包裹式特征选择比过滤式特征选择要好。但是由于包裹式特征选择方法在选择的过程中需要多次训练学习器, 其相应的计算开销会比过滤式特征选择方法大得多,且在样本数目不够时容易过拟合。
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