Markov Blanket Ranking using Kernel-based Conditional Dependence Measures

Eric V. Strobl; Shyam Visweswaran

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arXiv · 1402.0108

Markov Blanket Ranking using Kernel-based Conditional Dependence Measures

Eric V. Strobl·Shyam Visweswaran

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Abstract

Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pass in order to be included in the conditioning set. As a result, these algorithms may not consider all possible conditional multivariate combinations. We improve on this limitation by proposing a backward elimination method that uses a kernel-based conditional dependence measure to identify the Markov blanket in a fully multivariate fashion. The algorithm is easy to implement and compares favorably to other methods on synthetic and real datasets.

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BibTeX RIS

Eric V. Strobl, Shyam Visweswaran. 2014-02-01. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures. https://arxiv.org/abs/1402.0108

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