On Enhancing the Accuracy of Nearest Neighbour Time Series Classifier Using Improved Shape Exchange Algorithm

Authors : Imen BOULNEMOUR, Bachir BOUCHEHAM, Abdelmadjid LAHRECHE

Pages : 15-27

View : 13 | Download : 15

Publication Date : 2021-06-05

Article Type : Research Paper

Abstract :Several methods have been proposed for time series alignment and classification. In particular our previously published method I-SEA insert ignore into journalissuearticles values(Improved Shape Exchange Algorithm); has been proposed as a rival method to the SEA insert ignore into journalissuearticles values(Shape Exchange Algorithm); method for time series alignment. The aim of this work is to improve the accuracy of the SEA method for time series classification by proposing a 1NN-ISEA insert ignore into journalissuearticles values(1 Nearest Neighbor-Improved Shape Exchange Algorithm); classifier. Results of the proposed method show to be better as compared to those of the 1NN-SEA and the 1NN-ED classifiers insert ignore into journalissuearticles values(Euclidian Distance);. All results have been obtained using the UCR insert ignore into journalissuearticles values(University of California at Riverside); time series Dataset, universally admitted as the first Benchmark in time series classification and clustering.
Keywords : KNN, SEA, ISEA, ED, DTW, Time Series