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Author: search.filters.author.Jearanaitanakij, KietikulSubject: search.filters.subject.Particle Swarm Optimization

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    Improving knn algorithm based on weighted attributes by pearson correlation coefficient and pso fine Tuning
    (2020-10-21)
    Sinhashthita, Wanarase
    ;
    Jearanaitanakij, Kietikul
    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.
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    Improving Particle Swarm Optimization by using incremental attribute learning and centroid of particle's best positions
    (2017-11-03)
    Srimakham, Sornnarong
    ;
    Jearanaitanakij, Kietikul
    Particle Swarm Optimization (PSO) is a powerful algorithm that can search a solution for a function which contains a large number of peaks and valleys. However, PSO might encounter a difficulty when the function gets more complex or the number of attributes (dimensions) grows larger. This paper proposes a modification of PSO by using the incremental attribute strategy along with the centroid of particle's best positions to avoid the local minima which can easily occur in a multimodal problem. The experimental results from four standard benchmarks show that the proposed method can improve PSO in terms of optimality and stability when compared with the conventional PSO and another incremental attribute-based PSO.