A study on GBW-KNN using statistical testing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, S. | - |
dc.contributor.author | Kwak, Y.S. | - |
dc.contributor.author | Kim, M.-H. | - |
dc.contributor.author | Kang, M.S. | - |
dc.date.available | 2021-04-29T00:35:17Z | - |
dc.date.created | 2021-04-29 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1309-4653 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80866 | - |
dc.description.abstract | In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important. This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time. Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness. The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors. It is used a lot because it is simpler and easier to understand than other methods. In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph. In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods. As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant. © 2021 Karadeniz Technical University. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Karadeniz Technical University | - |
dc.relation.isPartOf | Turkish Journal of Computer and Mathematics Education | - |
dc.title | A study on GBW-KNN using statistical testing | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.17762/turcomat.v12i5.896 | - |
dc.identifier.bibliographicCitation | Turkish Journal of Computer and Mathematics Education, v.12, no.5, pp.271 - 277 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85104465179 | - |
dc.citation.endPage | 277 | - |
dc.citation.startPage | 271 | - |
dc.citation.title | Turkish Journal of Computer and Mathematics Education | - |
dc.citation.volume | 12 | - |
dc.citation.number | 5 | - |
dc.contributor.affiliatedAuthor | Kim, M.-H. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Classification | - |
dc.subject.keywordAuthor | K Nearest Neighbour | - |
dc.subject.keywordAuthor | Machine Learning | - |
dc.subject.keywordAuthor | WKNN | - |
dc.description.journalRegisteredClass | other | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.