On using Bayesian networks for complexity reduction in decision trees

摘要

In this paper we use the Bayesian network as a tool of explorative analysis: its theory guarantees that, given the structure and some assumptions, the Markov blanket of a variable is the minimal conditioning set through which the variable is independent from all the others. We use the Markov blanket of a target variable to extract the relevant features for constructing a decision tree (DT). Our proposal reduces the complexity of the DT so it has a simpler visualization and it can be more easily interpretable. On the other hand, it maintains a good classification performance.