Using Feature Hierarchies in Bayesian Network Learning

TitleUsing Feature Hierarchies in Bayesian Network Learning
Publication TypeBook Chapters
Year of Publication2000
AuthorsdesJardins M, Getoor L, Koller D
EditorChoueiry B, Walsh T
Book TitleAbstraction, Reformulation, and Approximation
Series TitleLecture Notes in Computer Science
Pagination260 - 270
PublisherSpringer Berlin / Heidelberg
ISBN Number978-3-540-67839-7

In recent years, researchers in statistics and the UAI community have developed an impressive body of theory and algorithmic machinery for learning Bayesian networks from data. Learned Bayesian networks can be used for pattern discovery, prediction, diagnosis, and density estimation tasks. Early pioneering work in this area includes [ 5 , 9 , 10 , 13 ]. The algorithm that has emerged as the current most popular approach is a simple greedy hill-climbing algorithm that searches the space of candidate structures, guided by a network scoring function (either Bayesian or Minimum Description Length (MDL)-based). The search begins with an initial candidate network (typically the empty network, which has no edges), and then considers making small local changes such as adding, deleting, or reversing an edge in the network.