School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, SA, Australia.
School of Computing and Informatics, University of Louisiana, Lafayette, LA, USA.
IEEE Trans Neural Netw Learn Syst. 2017 Nov;28(11):2775-2788. doi: 10.1109/TNNLS.2016.2602365.
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.
近年来，人们越来越关注在贝叶斯网络中使用马尔可夫毯进行特征选择。如果数据集的概率分布可以由该贝叶斯网络忠实地表示，则贝叶斯网络中类属性的马尔可夫毯是最优特征选择的唯一但最小的特征子集。然而，如果数据集违反了忠实条件，类属性的马尔可夫毯可能不是唯一的。针对这个问题，本文提出了一个新的代表集的概念，然后设计了通过分组 alpha 投资 (SGAI) 算法来执行具有代表集的马尔可夫毯特征选择，用于分类。使用一组全面的真实数据，我们的实证研究表明，SGAI 优于最先进的马尔可夫毯特征选择器和其他成熟的特征选择方法。
