属性约简方法应用于IDS

Xiang Cheng, B. Liu, Yi-lai Zhang
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引用次数: 3

摘要

本文将一种新的线性相关属性约简算法应用于特征选择。当特征与响应变量不相关,但与响应变量共同相关时,该算法是有价值的。引入了一种去除冗余属性的新技术,有效地降低了特征选择阶段的错误选择率。我们在KDD1999数据集上训练和测试了新算法,并比较了实验结果来说明该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attribute Reduction Method Applied to IDS
In this paper, we apply a new linear correlation attribute reduction algorithm to feature selection. The algorithm is valuable when the features are marginally unrelated but jointly related to the response variable. A new technique is introduced to remove redundant attributes and it is effective to reduce the false selection rate in the feature selection stage. We train and test the new algorithm on KDD1999 data set, and compare the experiment results to illustrate the methodology.
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