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Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
Author(s)
Sinhashthita, Wanarase
Date Issued
October 21, 2020
Type
Conference Paper
Abstract
Assigning 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.
Citation
Incit 2020 5th International Conference on Information Technology, 27-32, 2020