Publication:
Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

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dc.citedby0
dc.contributor.authorKhan I.en_US
dc.contributor.authorMohamed Zabil M.H.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorJabeur N.en_US
dc.contributor.authorid58061521900en_US
dc.contributor.authorid58883856000en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid6505727698en_US
dc.date.accessioned2024-10-14T03:19:25Z
dc.date.available2024-10-14T03:19:25Z
dc.date.issued2023
dc.description.abstractThis paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICOCO59262.2023.10398042
dc.identifier.epage413
dc.identifier.scopus2-s2.0-85184852692
dc.identifier.spage408
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184852692&doi=10.1109%2fICOCO59262.2023.10398042&partnerID=40&md5=b44953004afe65bbe29571e549fce251
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34385
dc.pagecount5
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE International Conference on Computing, ICOCO 2023
dc.subjectChebyshev
dc.subjectEuclidean
dc.subjectk-Nearest Neighbor (k-NN)
dc.subjectMachine Learning
dc.subjectManhattan
dc.subjectStudents' Performance Prediction
dc.subjectMachine learning
dc.subjectNearest neighbor search
dc.subjectStudents
dc.subjectChebyshev
dc.subjectDistance metrics
dc.subjectEuclidean
dc.subjectK-near neighbor
dc.subjectMachine-learning
dc.subjectManhattans
dc.subjectPerformance prediction
dc.subjectPerformance prediction models
dc.subjectStudent performance
dc.subjectStudent' performance prediction
dc.subjectForecasting
dc.titleTowards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modelingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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