Causal feature selection method based on extended Markov blanket
by Yinghan Hong; Zhifeng Hao; Guizhen Mai; Han Huang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 15, No. 4, 2018

Abstract: 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.

Online publication date: Wed, 02-Jan-2019

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