Journal of Machine Learning Research (JMLR)
Most existing algorithms for learning Markov network structure either are limited to learning interactions among few variables or are very slow, due to the large space of possible structures. In this paper, the authors propose three new methods for using decision trees to learn Markov network structures. The advantage of using decision trees is that they are very fast to learn and can represent complex interactions among many variables. The first method, DTSL, learns a decision tree to predict each variable and converts each tree into a set of conjunctive features that de ne the Markov network structure.