title: Markov Blanket Feature Selection Using Representative Sets.
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.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.
authors.affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia.
authors.forename: Kui
authors.initials: K
authors.lastname: Yu
authors.affiliation: School of Computing and Informatics, University of Louisiana, Lafayette, LA, USA.
authors.forename: Xindong
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Ding
authors.affiliation: Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Mu
authors.affiliation: Department of Computer Science, Hefei University of Technology, Hefei, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Wang
keywords: Algorithm design and analysis; Bayes methods; Clustering algorithms; Learning systems; Markov processes; Probability distribution; Redundancy