A wrapper feature selection approach using Markov blankets

In feature selection, Markov Blanket (MB) based approaches have attracted considerable attention with most MB discovery algorithms being categorized as filter based techniques. Typically, the Conditional Independence (CI) test employed by such methods is different for different data types. In this article, we propose a novel Markov Blanket based wrapper feature selection method. The proposed approach employs Predictive Permutation Independence (PPI), a novel Conditional Independence (CI) test that allows it to work out-of-the-box for both classification and regression tasks on mixed data. PPI can work with any supervised algorithm to estimate the association of a feature with the target variable while also providing a measure of feature importance. The proposed approach also includes an optional MB aggregation step that can be used to find the optimal MB under non-faithful conditions. Our method¹ outperforms other MB discovery methods, in terms of F1-score, by 7% on average, over 3 large-scale BN datasets. It also outperforms state-of-the-art feature selection techniques on 13 real-world datasets.