Markov Blanket Feature Selection with Non-faithful Data Distributions

In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection. However, little attention has been paid to Markov blanket feature selection in a non-faithful environment which widely exists in the real world. To tackle this issue, in this paper, we deal with non-faithful data distributions and propose the concept of representative sets instead of Markov blankets. With a standard sparse group lasso for selection of features from the representative sets, we design an effective algorithm, SRS, for Markov blanket feature Selection via Representative Sets with non-faithful data distributions. Empirical studies demonstrate that SRS outperforms the state-of-the-art Markov blanket feature selectors and other well-established feature selection methods.