Prüfer Number Encoding for Genetic Bayesian Network Structure Learning Algorithm

Bayesian networks encode causal relations between variables using probability and graph theory. We employ genetic algorithm to exploit these causal relations from data for classification problems, thus restricting the search space from directed acyclic graphs to trees. Prufer number encoding of the structure is employed for the representation of individuals in the genetic algorithm. Several score functions - information criteria - are also employed in order to analyse Prufer number encoding for Bayesian network structure learning. In this work we show that Prufer number encoding can reveal the causal dependence between class the variable and the attributes, the dependence being made without a-priori information regarding about the class variable.