ON EUCLIDEAN NETWORKS FOR IMPROVING CLASSIFICATION ACCURACY
| dc.contributor.advisor | Barton, Armon C. | |
| dc.contributor.author | Slaughter, Jacob W. | |
| dc.contributor.department | Computer Science (CS) | |
| dc.contributor.secondreader | Orescanin, Marko | |
| dc.date.accessioned | 2023-05-03T16:03:32Z | |
| dc.date.available | 2023-05-03T16:03:32Z | |
| dc.date.issued | 2023-03 | |
| dc.description.abstract | Machine learning is found in nearly every facet of daily life. Large amounts of data are required but not always available for specific problems, precluding the use of advanced methods such as deep learning and convolutional neural networks. The Euclidean Network (EN) can be used to mitigate these issues. The EN was thoroughly tested to prove its viability as a classification algorithm and that its methods may be used to augment data and transform the input data to increase its feature space dimensionality. Originally, it was hypothesized that the EN could be used to synthetically generate data to augment a data set, though this method was proven to be ineffective. The next area of research sought to expand the dimensionality of the input feature space to improve performance with additional classifiers. This area showed positive results, which supported the hypothesis that more complex, dense input would give algorithms more insight into the data and improve performance. The EN has been found to perform exceptionally well as an independent classifier, as it achieved the highest accuracy for 12 of the 21 data sets. For the remaining 9, though it did not have the highest accuracy, the EN performed comparably to more sophisticated algorithms. The EN also proved capable to expand a data set's feature space to further improve performance. This tactic provided a more robust classification technique and saw an average increase in accuracy of 3% between all data sets. | en_US |
| dc.description.distributionstatement | Approved for public release. Distribution is unlimited. | en_US |
| dc.description.service | Captain, United States Marine Corps | en_US |
| dc.identifier.curriculumcode | 368, Computer Science | |
| dc.identifier.thesisid | 38767 | |
| dc.identifier.uri | https://hdl.handle.net/10945/72055 | |
| dc.publisher | Monterey, CA; Naval Postgraduate School | en_US |
| dc.rights | This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. | en_US |
| dc.subject.author | EN | en_US |
| dc.subject.author | Euclidean Network | en_US |
| dc.subject.author | machine learning | en_US |
| dc.subject.author | classification | en_US |
| dc.title | ON EUCLIDEAN NETWORKS FOR IMPROVING CLASSIFICATION ACCURACY | en_US |
| dc.type | Thesis | en_US |
| dspace.entity.type | Publication | |
| etd.thesisdegree.discipline | Computer Science | en_US |
| etd.thesisdegree.grantor | Naval Postgraduate School | en_US |
| etd.thesisdegree.level | Masters | en_US |
| etd.thesisdegree.name | Master of Science in Computer Science | en_US |
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