Causal feature selection method based on extended Markov blanket
Yinghan Hong, Zhifeng Hao, Guizhen Mai, Han Huang · International Journal of Wireless and Mobile Computing · 2018
Feature selection is generally a key pre-process in artificial intelligence, machine learning and pattern recognition. Its purpose is to select a set of features that is most effective to predict the target. The existing features selection methods are generally a kind of features sorting methods according to the dependence between these features and the target variable. It is difficult for these methods to determine a certain number of features; moreover, in this study we show that some key feature is probably removed by these methods. To alleviate this problem, a causal feature selection method based on causal network is proposed. When the target variable and its candidate feature set form a causal network model, the proposed method can detect the causal features by conditional independence test based method according to extended Markov blanket. This method is able to cut out a certain number of features, and simultaneously can avoid missing any key feature. Experimental results demonstrate that the proposal outperforms the counterparts when applied to support vector regression.