Performance of different KNN models in prediction english language readability

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dc.contributor.authorAltay O.
dc.date.accessioned2024-07-22T08:04:55Z
dc.date.available2024-07-22T08:04:55Z
dc.date.issued2022
dc.description.abstractAssessing the readability of English, a universal language, is important in terms of meeting readers at different reading levels with texts at their own level. Presenting texts to readers at their own level will help them develop their learning, comprehension and reading capacities. In this study, a data set collected from BBC news was used to predict the readability of the English language. The data set consists of 17724 different sentences. Different k-nearest neighbor (KNN) models were used to predict the readability of English sentences. These models are basic KNN, two different weighted KNN and KNN base random subspace ensembles. KNN base random subspace ensemble has obtained superior results compared to other KNN models. KNN base random subspace ensemble accuracy was 0.9749 and f1-score 0.9692. © 2022 IEEE.
dc.identifier.DOI-ID10.1109/ICMI55296.2022.9873670
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12886
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectNearest neighbor search
dc.subjectData set
dc.subjectEnglish language sentence readability
dc.subjectEnglish languages
dc.subjectEnglish sentences
dc.subjectEnsemble
dc.subjectK-nearest neighbour models
dc.subjectPerformance
dc.subjectRandom subspace ensembles
dc.subjectRandom subspaces
dc.subjectReading level
dc.subjectForecasting
dc.titlePerformance of different KNN models in prediction english language readability
dc.typeConference paper

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