Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/27409
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ilievska, Elena | en_US |
dc.contributor.author | Sekuloski, Petar | en_US |
dc.date.accessioned | 2023-08-15T10:08:48Z | - |
dc.date.available | 2023-08-15T10:08:48Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/27409 | - |
dc.description.abstract | Modern technology in today’s world is largely driven by machine learning algorithms. They are incorporated into every field. Big data is not always available to us, though. We frequently have to work with limited-size of data. The purpose of this paper is to demonstrate several machine learning algorithms and their accuracy on small numerical datasets. We investigate the effectiveness of these algorithms with and without the implementation of two variables, degree and closeness centrality, which are extracted from the dataset using the knearest neighbor graph. | en_US |
dc.publisher | Ss Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering, Republic of North Macedonia | en_US |
dc.relation.ispartofseries | CIIT 2023 papers;34; | - |
dc.subject | machine learning algorithms, numeric datasets, k-nearest neighbor graph | en_US |
dc.title | Performance Analysis of Machine Learning Algorithms on Small Datasets that Includes Features from K-Nearest Neighbor Graph | en_US |
dc.type | Proceeding article | en_US |
dc.relation.conference | 20th International Conference on Informatics and Information Technologies - CIIT 2023 | en_US |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Files in This Item:
File | Description | Size | Format | |
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CIIT2023_paper_34.pdf | 9.18 MB | Adobe PDF | View/Open |
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