A Novel Structure Learning Algorithm for Optimal Bayesian Network: Best Parents

We present a novel algorithm for learning structure of a Bayesian Network. Best Parents is a greedy construction method which performs structure learning without preconditioned knowledge or preprocessing. Unlike the well-known methods such as K2, TAN Hill Climbing or Simulated Annealing, we use no feature ordering, DAG validity or structure metrics. We provide a new greedy algorithm for optimal structure learning using conditional entropy. Also we perform a running time and performance comparison with other methods in the field. Our results indicate substantial optimality of our proposed algorithm in terms of running time and AUC combination. (C) 2016 The Authors. Published by Elsevier B.V.