In biomedical and biological domains, researchers typically study continuous data sets. In these domains, an increasingly popular tool for understanding the relationship between variables is Bayesian network structure learning. There are three methods for learning Bayesian network structure from continuous data. The most popular approach is discretizing the data prior to structure learning. Alternative approaches are integrating discretization with structure learning as well as learning directly with continuous data. It is not known which method is best since there has not been a unified study of the three approaches. The purpose of this work was to perform an extensive experimental evaluation of them. For large data sets consisting of originally discrete variables, discretization-based approaches learned the most accurate structures. With smaller sample sizes or data without an underlying discrete mechanism, a method learning directly with continuous data performed best. Also, for some quality metrics, the integrated methods did not provide improvements over simple discretization methods. In terms of time-efficiency, the integrated approaches were the most computationally intensive, while methods from the other categories were the least intensive.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-12022005-171510 |
| Date | 19 December 2005 |
| Creators | Fu, Lawrence Dachen |
| Contributors | Ioannis Tsamardinos, Constantin Aliferis, Doug Hardin |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-12022005-171510/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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