dc.contributor.advisor | ZhiQiang, Chen | |
dc.contributor.author | Chavakula, Varun | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017 Fall | |
dc.description | Title from PDF of title page viewed January 30, 2018 | |
dc.description | Thesis advisor: Chen ZhiQiang | |
dc.description | Vita | |
dc.description | Includes bibliographical references (pages 43-46) | |
dc.description | Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2017 | |
dc.description.abstract | Accounting for uncertainty is important in any data driven decision making. The
popular treatment of uncertainties is to employ classical probability theory by expressing
variables as random variables or processes in terms of random distributions. This precise
approach encounters difficulty and leads to deceptive predictions when the sources of
uncertainty are epistemic in terms of incomplete (missing), conflicting, or erroneous
information due to the lack of knowledge. There have been many frameworks developed
against the precise probability formalism, and one of such frameworks is the Imprecise
Probability (IP) based modeling.
In this thesis, we develop and provide a novel hybrid framework, Naïve Credal
Classifier with Expectation-Maximization data imputation, for decision making with missing
information. The IP-based Credal Set concept is first introduced to model uncertainties for
data with missing information. Then the Naïve Credal Classifier (NCC) is employed in this
work, which is provided by the latest JNCC2 package. The key idea and research findings in
this research is to model missing data using advanced imputation techniques to minimize the
performance (accuracy) loss in NCC. The resulting NCC-EM framework is hybrid where the
EM imputation technique is used as a preprocessing step. To verify and validate this hybrid
framework, the NCC-EM is extensively tested on open machine learning datasets by
simulating missing values, and it is shown that NCC-EM outperforms the existing NCC
framework and traditional supervised classification methods. | eng |
dc.description.tableofcontents | Introduction -- introduction to imprecise probability -- Naïve Bayes Classifier and Naïve Credal classifier -- NCC-EM: a novel Credal based framework -- Conclusion and future work | |
dc.format.extent | ix, 47 pages | |
dc.identifier.uri | https://hdl.handle.net/10355/62662 | |
dc.publisher | University of Missouri--Kansas City | eng |
dc.subject.lcsh | Uncertainty (Information theory) | |
dc.subject.lcsh | Probabilities | |
dc.subject.lcsh | Machine Learning | |
dc.subject.lcsh | Missing observations (Statistics) | |
dc.subject.other | Thesis -- University of Missouri--Kansas City -- Computer science | |
dc.title | NCC-EM: A hybrid framework for decision making with missing information | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Computer Science (UMKC) | |
thesis.degree.grantor | University of Missouri--Kansas City | |
thesis.degree.level | Masters | |
thesis.degree.name | M.S. | |