Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/20624
Title: Time-Series Classification with Constrained DTW Distance and Inverse-Square Weighted k-NN
Authors: Geler Zoltan 
Kurbalija Vladimir 
Ivanović Mirjana 
Radovanović Miloš 
Issue Date: 2020
Journal: INISTA 2020 - 2020 International Conference on INnovations in Intelligent SysTems and Applications, Proceedings, 2020 International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2020, Novi Sad, srb, 2020, 2020/08/24-2020/08/26
Abstract: © 2020 IEEE. The problem of time-series classification witnessed the application of many techniques for data mining and machine learning, including neural networks, support vector machines, and Bayesian approaches. Somewhat surprisingly, the simple 1-nearest neighbor (1NN) classifier, in combination with the Dynamic Time Warping (DTW) distance measure, is still competitive and not rarely superior to more advanced classification methods, which includes the majority-voting k-nearest neighbor (kNN) classifier. In this paper we focus on the kNN classifier combined with the inverse-squared weighting scheme, and its interaction with constrained DTW distance. By performing experiments on the entire UCR Time Series Classification Archive we show that with proper selection of the constraint parameter r and neighborhood size k, inverse-square weighted kNN consistently outperforms 1NN.
URI: https://open.uns.ac.rs/handle/123456789/20624
ISBN: 9781728167992
DOI: 10.1109/INISTA49547.2020.9194639
Appears in Collections:FF Publikacije/Publications
PMF Publikacije/Publications

Show full item record

SCOPUSTM   
Citations

8
checked on May 3, 2024

Page view(s)

44
Last Week
6
Last month
4
checked on May 10, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.