Markov Boundary Learning With Streaming Data for Supervised Classification

In this paper, we study the problem of Markov boundary (MB) learning with streaming data. A MB is a crucial concept in a Bayesian network (BN) and plays an important role in BN structure learning. In addition, in the supervised learning setting, the MB of a class attribute is optimal to feature selection for classification. Almost all existing MB learning algorithms focus on static data, but few efforts have been proposed to learning MBs with streaming data. In this paper, by linking dynamic AD-trees with streaming data, we proposed a new SDMB (streaming data-based MB) algorithm for learning MBs with streaming data. Specifically, given a target variable, SDMB employs a dynamic AD-tree to summarize the historical data, then the SDMB sequentially learns the MB of the target upon all available data by calculating independence tests using the dynamic AD-tree. In experiments, using the synthetic and real-world data sets, we evaluate the SDMB algorithm and compared it with the state-of-the-art online feature selection algorithms and data stream mining methods, and the experimental results validate the SDMB algorithm.