1. An automated data classification system, comprising: a processor; a memory accessed by the processor, the memory comprising: a data set stored in the memory and having at least one labeled category node in the data set; and a semi-supervised learning component stored in the memory and configured to be executed by the processor that employs directed arcs to determine at least one other category node from the data set, the category nodes configured for determining the posterior of any adjacent category node, the directed arcs having an associated conditional probability matrix, the semi-supervised learning component further employing multiple classifier outputs and a smoothing component to alter a weight applied to the multiple classifier outputs by adjusting conditionals of the directed arcs, the semi-supervised learning component further computing a distribution at a selected node of the category nodes, given the distributions of neighbor nodes of the selected node and the conditional probability matrix associated with each of the directed arcs from the neighbor nodes to the selected node, such that a number of bits to describe the distribution is minimized, wherein a subset of the directed arcs comprise loops.