Adaptive Feature Redundancy Minimization
Adaptive Feature Redundancy Minimization
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DOI：
10.1145/3357384.3358112
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发表时间：
2019-11
期刊：
影响因子：
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通讯作者：
Rui Zhang;Hanghang Tong;Yifan Hu
中科院分区：
文献类型：
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作者：
Rui Zhang;Hanghang Tong;Yifan Hu
现有的特征选择方法大多是根据一定的标准来选择排名靠前的特征。但是，如果不考虑特征之间的冗余性，所选择的特征之间往往是高度相关的，这不利于性能。为了解决这个问题，我们提出了一个基于自适应冗余最小化（ARM）的特征选择框架。与其他特征选择方法不同，本文提出的模型具有以下优点：(1)冗余矩阵是自适应构造的，而不是将其预设为先验信息。(2)该模型可以通过最小化特征的全局冗余度来挑选出判别和非冗余的特征。(3) ARM可以从监督和无监督的角度减少特征的冗余。
Most existing feature selection methods select the top-ranked features according to certain criterion. However, without considering the redundancy among the features, the selected ones are frequently highly correlated with each other, which is detrimental to the performance. To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection. Unlike other feature selection methods, the proposed model has the following merits: (1) The redundancy matrix is adaptively constructed instead of presetting it as the priori information. (2) The proposed model could pick out the discriminative and non-redundant features via minimizing the global redundancy of the features. (3) ARM can reduce the redundancy of the features from both supervised and unsupervised perspectives.