A Causal Feature Selection Algorithm for Feedback Networks and Its Applications

doi:10.3969/j.issn.1006-2475.2019.12.018

Abstract

Abstract: As the two feature selection strategies of Markov blanket based methods and information-theoretic based methods often fail to solve the feature selection problem under the multi-layer network with feedback mechanism, a causal feature selection method for feedback multi-layer networks is proposed. The method first uses the D-separation method to find the neighbor node of the target node T, that is, the neighbor feature Ne(T). Then, the mutual information of the target node and the remaining features is obtained, and the feature set R of the element D-separation of the mutual information and not the set Ne(T) is found, and finally the Ne(T) and R are merged as the target node. The method effectively avoids the problem of feature selection error based on Markov blanket under feedback network and feature selection of maximum mutual information under multi-layer network. Compared with the two classic methods in the typical warning of power marketing system, the experimental results show that the method is more effective.

Key words: feature selection, causal network, Markov blanket, D-separation, mutual information, electric power marketing system

CLC Number:

TP181

YAN Yi, LI Bo, CHEN Shou-ming, LIN Qiang, HUANG Ju-tao, Wen Bo-jian. A Causal Feature Selection Algorithm for Feedback Networks and Its Applications[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.12.018.

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