The performance of some three-dimensional mutual information-based algorithms can be affected, since only relevance and interaction are considered. Aiming at solving the problem, a feature selection algorithm based on redundancy analysis and interaction weight is proposed in this paper. The proposed algorithm adopts three-way interaction information to measure the interaction among the class label and features, and processes features for interaction weight analysis. Then, it employs symmetric uncertainty to measure the relevance between features and the class label as well as the redundancy between features, and selects the features with greater relevance and interaction as well as smaller redundancy. To validate the performance, the proposed algorithm is compared with several feature selection algorithms. Since relevance, redundancy, and interaction analysis are all presented, the proposed algorithm can obtain better feature selection performance.

由于只考虑关联性和交互性，因此某些基于三维互信息的算法的性能可能会受到影响。针对该问题，提出了一种基于冗余度分析和交互权重的特征选择算法。该算法采用三向交互信息来度量类标签和特征之间的交互，并对交互权重进行处理。然后，它采用对称不确定性来度量要素与类标签之间的相关性以及要素之间的冗余度，并选择具有更大相关性和交互性以及较小冗余度的要素。为了验证性能，将该算法与几种特征选择算法进行了比较。由于相关性，冗余性，