A Method of Discovering Tolerance Markov Blanket Based on Completely Dependent Unknown Components

Hongzhou He, Mingtian Zhou

Abstract


A novel tolerance feature subset selection method from incomplete data set, denoted by MaxG-IIAMB, is proposed to pick out the Markov-boundary (MB), the minimal subset of features, of target variable but without making any assumption about the unknown component distribution. The classification experimental results of risk factors observed in a sample of 1841 employees of a Czech car factory demonstrate the practicability and superiority of our method over the classical expectation-maximization (EM) and available case technique (ACA).

 

DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.3079


Keywords


Feature Subset Selection (FSS); Markov Boundary (MB); Probabilistic Classification; Unknown Component Distribution

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