Markov blanket feature selection using representative sets
Date
2017
Authors
Yu, K.
Wu, X.
Ding, W.
Mu, Y.
Wang, H.
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IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(11, article no. 7560637):2775-2788
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Abstract
It has received much attention in recent years to use Markov blankets in a Bayesian network for feature selection. The Markov blanket of a class attribute in a Bayesian network is a unique yet minimal feature subset for optimal feature selection if the probability distribution of a data set can be faithfully represented by this Bayesian network. However, if a data set violates the faithful condition, Markov blankets of a class attribute may not be unique. To tackle this issue, in this paper, we propose a new concept of representative sets and then design the selection via group alpha-investing (SGAI) algorithm to perform Markov blanket feature selection with representative sets for classification. Using a comprehensive set of real data, our empirical studies have demonstrated that SGAI outperforms the state-of-the-art Markov blanket feature selectors and other well-established feature selection methods.
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Copyright 2016 IEEE