Automatic classi?cation of harmonic data using k-means and least square support vector machine - Erişti, Hüseyin; Tümen, Vedat; Yıldırım, Özal; Erişti, Belkıs; Demir, Yakup (My University, 2015)
In this paper, an eﬀective classiﬁcation approach to classify harmonic data has been proposed. In the proposed classiﬁer approach, harmonic data obtained through a 3-phase system have been classiﬁed by using k-means and least square support vector machine (LS-SVM) models. In order to obtain class details regarding harmonic data, a k-means clustering algorithm has been applied to these data ﬁrst. The training of the LS-SVM model has been realized with the class details obtained through the k-means algorithm. To increase the eﬃciency of the LS-SVM model, the regularization and kernel parameters of this model have been determined with a grid search method and the training phase has been realized. Backpropagation neural network and J48 decision tree classiﬁers have been applied to the same data and results have been obtained for the purpose of comparing the performance of the LS-SVM model. The real data obtained from the output of distribution system have been used to assess the performance of the proposed classiﬁer system. The obtained results and comparisons suggest that the proposed classiﬁer system approach is quite eﬃcient at classifying harmonic data.
Keywords: Mühendislik; Elektrik ve Elektronik; Bilgisayar Bilimleri; Yazılım Mühendisliği