Nonstationary Gaussian process regression can be used to transform irregularly episodic and noisy measurements into continuous probability densities to make them more compatible with standard machine learning algorithms. However, current inference algorithms are time-consuming or have difficulty with the highly bursty, extremely nonstationary data that are common in the medical domain. One efficient and flexible solution uses a partition kernel based on random forests, but its current embodiment produces undesirable pathologies rooted in the piecewise-constant nature of its inferred posteriors. I present a modified random forest kernel that adds a new sources of randomness to the trees, which overcomes existing pathologies and produces good results for highly bursty, extremely nonstationary clinical laboratory measurements.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-07222016-224434 |
| Date | 25 July 2016 |
| Creators | VanHouten, Jacob Paul |
| Contributors | Thomas A. Lasko, Christopher J. Fonnesbeck |
| 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-07222016-224434/ |
| Rights | restricted, 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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