Capítulos en libros Due to the large size of electric power systems there is a very high computational burden when obtaining the optimum network by using classical optimization techniques. Several authors have used heuristics and/or sensisitivities in order to guide the search of optimal network investments. This paper proposes an Automatic Learning approach in order to decide whether a network change will improve the overall costs or not. more specifically, Decision Trees methods are used to identifiy a set of simple and reliable rules which combine criteria trees are integrated in a subtransmission planning tool, improving dramatically both the optimality of the resultant network and the computational time. info:eu-repo/semantics/publishedVersion