On Euclidean Networks for Improving Classification Accuracy
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 sets 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 2023
- Accession Number
- AD1213017
Entities
People
- Jacob W. Slaughter
Organizations
- Naval Postgraduate School