Learning Markov Network Structure using Few Independence Tests

In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for learning the structure of the Markov network of a domain from independence tests on data. DGSIMN, like other independence-based algorithms, works by conducting a series of statistical conditional independence tests toward the goal of restricting the number of possible structures to one, thus inferring that structure as the only possibly correct one. During this process, DGSIMN, like the GSIMN algorithm, uses the axioms that govern the probabilistic independence relation to avoid unnecessary tests i.e., tests that can be inferred from the results of known ones. This results in both efficiency and reliability advantages over the simple application of statistical tests. However, one weakness of GSIMN is its rigid and heuristic ordering of the execution of tests, which results in potentially inefficient executio...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Parichey Gandhi, Facundo Bromberg, Dimitris Margaritis
• Keywords: Conditional Independence Tests, Data Mining, Independence Tests, Markov Network, SDM 2008