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Normalized and Classified Feature Selection in Text Categorization

机译:文本分类中的归一化和分类特征选择

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摘要

Feature selection is a valid method to reduce the dimension of text vector in automatic text categorization system. The paper finds a defect among several normal evaluatation functions based on experiments data and proposes that normalization should be taken into these methods as a necessary step. Furthermore, the paper also brings forward a new idea named classified feature selection that applies traditional evaluation function among each class. Experiments prove the validity of these two solutions.
机译:在自动文本分类系统中,特征选择是减少文本向量维数的有效方法。本文根据实验数据发现了几种正常评估函数之间的缺陷,并建议将标准化方法作为必要步骤。此外,本文还提出了一种新的思想,即分类特征选择,该思想在每个类别之间应用了传统的评估功能。实验证明了这两种解决方案的有效性。

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