Dependency networks based classifiers: learning models by using independence.
José Antonio Gámez, Juan Luis Mateo, Jose Miguel Puerta · Probabilistic Graphical Models · 2006
In this paper we propose using dependency networks (Heckerman et al., 2000), that is a probabilistic graphical model similar to Bayesian networks, to model classifiers. The main difference between these two models is that in dependency networks cycles are allowed, and this fact has the consequence that the automatic learning process is much easier and can be parallelized. These properties make dependency networks a valuable model especially when it is needed to deal with large databases. Because of these promising characteristics we analyse the usefulness of dependency networks-based Bayesian classifiers. We present an approach that uses independence tests based on chi square distribution, in order to find relationships between predictive variables. We show that this algorithm is as good as some state-of the-art Bayesian classifiers, like TAN and an implementation of the BAN model, and has, in addition, other interesting proprierties like scalability or good quality for visualizing relationships.