Automatic Construction of Bayesian Network Structures by Means of a Concurrent Search Mechanism

The implicit knowledge in the databases can be extracted of automatic form. One of the several approaches considered for this problem is the construction of graphical models that represent the relations between the variables and regularities in the data. In this work the problem is addressed by means of an algorithm of search and scoring. These kind of algorithms use a heuristic mechanism search and a function of score to guide themselves towards the best possible solution.

The algorithm, which is implemented in the semifunctional language Lisp, is a searching mechanism of the structure of a bayesian network (BN) based on concurrent processes.

Each process is assigned to a node of the BN and effects one of three possible operations between its node and some of the rest: to put, to take away or to invert an edge. The structure is constructed using the metric MDL (made up of three terms), whose calculation is made of distributed way, in this form the search is guided by selecting those operations between the nodes that minimize the MDL of the network.

In this work are presented some results of the algorithm in terms of comparison of the structure of the obtained network with respect to its gold network.