Learning Markov Network Structure with Decision Trees

Learning Markov Network Structure with Decision Trees

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by Daniel Lowd , Jesse Davis

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Datum	Value	Source
TITLE	Learning Markov Network Structure with Decision Trees	SVM HeaderParse 0.2
AUTHOR NAME	Daniel Lowd	SVM HeaderParse 0.2
AUTHOR AFFIL	∗ Department of Computer and Information Science; University of Oregon, Eugene	SVM HeaderParse 0.2
AUTHOR ADDR	OR 97403	SVM HeaderParse 0.2
AUTHOR NAME	Jesse Davis	SVM HeaderParse 0.2
AUTHOR AFFIL	∗ Department of Computer and Information Science; University of Oregon, Eugene	SVM HeaderParse 0.2
AUTHOR ADDR	OR 97403	SVM HeaderParse 0.2
ABSTRACT	Abstract—Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima due to the large space of possible structures. Ravikumar et al. [1] recently proposed the alternative idea of applying L1 logistic regression to learn a set of pairwise features for each variable, which are then combined into a global model. This paper presents the DTSL algorithm, which uses probabilistic decision trees as the local model. Our approach has two significant advantages: it is more efficient, and it is able to discover features that capture more complex interactions among the variables. Our approach can also be seen as a method for converting a dependency network into a consistent probabilistic model. In an extensive empirical evaluation on 13 datasets, our algorithm obtains comparable accuracy to three standard structure learning algorithms while running 1-4 orders of magnitude faster. Keywords-Markov networks; structure learning; decision trees; probabilistic methods I.	SVM HeaderParse 0.2
CITATIONS	19 found	ParsCit 1.0