Towards scalable and data efficient learning of Markov boundaries
2007 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 45, no 2, 211-232 p.Article in journal (Refereed) Published
We propose algorithms for learning Markov boundaries from data without having to learn a Bayesian network first. We study their correctness, scalability and data efficiency. The last two properties are important because we aim to apply the algorithms to identify the minimal set of features that is needed for probabilistic classification in databases with thousands of features but few instances, e.g. gene expression databases. We evaluate the algorithms on synthetic and real databases, including one with 139,351 features. © 2006 Elsevier Inc. All rights reserved.
Place, publisher, year, edition, pages
2007. Vol. 45, no 2, 211-232 p.
IdentifiersURN: urn:nbn:se:liu:diva-38393DOI: 10.1016/j.ijar.2006.06.008Local ID: 44146OAI: oai:DiVA.org:liu-38393DiVA: diva2:259242