Co-eye: A multi-resolution ensemble classifier for Symbolically Approximated Time Series

Abdallah, Zahraa S. and Gaber, Mohamed Medhat (2020) Co-eye: A multi-resolution ensemble classifier for Symbolically Approximated Time Series. Machine Learning. ISSN 1573-0565

Preview

Text Co_eye__A_multi_resolution_ensemble_classifier_for_Symbolically_Approximated_Time_Series.pdf - Accepted Version Available under License Creative Commons Attribution. Download (2MB)

Abstract

Time series classification (TSC) is a challenging task that attracted many researchers in the last few years. One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no “one model that fits all” in TSC. Some algorithms are very accurate in classifying a specific type of time series when the whole series is considered, while some only target the existence/nonexistence of specific patterns/shapelets. Yet other techniques focus on the frequency of occurrences of discriminating patterns/features. This paper presents a new classification technique that addresses the inherent diversity problem in TSC using a nature-inspired method. The technique is stimulated by how flies look at the world through “compound eyes” that are made up of thousands of lenses, called ommatidia. Each ommatidium is an eye with its own lens, and thousands of them together create a broad field of vision. The developed technique similarly uses different lenses and representations to look at the time series, and then combines them for broader visibility. These lenses have been created through hyper-parameterisation of symbolic representations (Piecewise Aggregate and Fourier approximations). The algorithm builds a random forest for each lens, then performs soft dynamic voting for classifying new instances using the most confident eyes, i.e, forests. We evaluate the new technique, coined Co-eye, using the recently released extended version of UCR archive, containing more than 100 datasets across a wide range of domains. The results show the benefits of bringing together different perspectives reflecting on the accuracy and robustness of Co-eye in comparison to other state-of-the-art techniques.

Item Type:	Article
Identification Number:	10.1007/s10994-020-05887-3
Dates:	Date Event 21 May 2020 Accepted 26 August 2020 Published Online
Uncontrolled Keywords:	time series classification, symbolic representation, ensemble classification, random forest
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Mohamed Gaber
Date Deposited:	11 Aug 2020 10:50
Last Modified:	12 Jan 2022 12:58
URI:	https://www.open-access.bcu.ac.uk/id/eprint/9638

Actions (login required)

View Item