Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning

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dc.contributor.authorSinhashthita, Wanarase
dc.contributor.authorJearanaitanakij, Kietikul
dc.date.accessioned2026-08-06T10:29:54Z
dc.date.available2026-08-06T10:29:54Z
dc.date.issued2020-10-21
dc.description.abstractAssigning proper weights to attributes in some datasets according to their importances can significantly improve the classification accuracy. Weighted attributes can support the classification methods effectively if their weights truly represent by their importances. In this research, we improve the K-Nearest Neighbors (KNN) algorithm by using Pearson correlation coefficient along with Particle Swarm Optimization (PSO) to find the optimal set of weights for attributes in the dataset. The experimental results show that the proposed method can significantly improve the classification accuracy when compared to the traditional KNN algorithm.
dc.identifier.citationIncit 2020 5th International Conference on Information Technology, 27-32, 2020
dc.identifier.doi10.1109/InCIT50588.2020.9310938
dc.identifier.other2-s2.0-85100182742
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11317
dc.sourceIncit 2020 5th International Conference on Information Technology
dc.subjectAttribute Weighting
dc.subjectClassification
dc.subjectK Nearest Neighbors algorithm
dc.subjectParticle Swarm Optimization
dc.subjectPearson correlation coefficient
dc.titleImproving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
dc.typeConference Paper

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