Flexible learning of k-dependence Bayesian network classifiers
Arcadio Rubio, José Antonio Gámez · 2011
In this paper we present an extension to the classical k-dependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from naïve Bayes to unrestricted networks. In our experience, it performs well for low values of k. However, the algorithm tends to degrade in more complex spaces, as it greedily tries to add k dependencies to all feature nodes of the resulting net.