特征选择
计算机科学
水准点(测量)
选择(遗传算法)
人工智能
特征(语言学)
成对比较
相似性(几何)
机器学习
算法
大地测量学
语言学
图像(数学)
哲学
地理
作者
Zhaolong Ling,Ying Li,Yiwen Zhang,Kui Yu,Peng Zhou,Bo Li,Xindong Wu
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-13
被引量:2
标识
DOI:10.1109/tkde.2022.3218786
摘要
Causal feature selection has received increasing attention in recent years. However, the state-of-the-art causal feature selection algorithms use the conditional independence tests, which require enumerating conditioning sets, leading to an exponential increase in computational complexity along with an increase in feature space. To address this problem, in this paper, we theoretically analyze the unique performance of causal features in mutual information, and propose a novel C ausal F eature S election algorithm using M utual I nformation, called CFS-MI. Specifically, CFS-MI separately instantiates the pairwise comparison of mutual information in two stages to reduce computational complexity, and thus improves the efficiency on high-dimensional data. Extensive experiments on 5 benchmark Bayesian networks and 16 real-world datasets validate that CFS-MI has comparable accuracy compared to 7 state-of-the-art causal feature selection algorithms, while presenting more superior computational efficiency.
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