Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series
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Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series Zahraa S. Abdallah1
· Mohamed Medhat Gaber1
Received: 29 June 2019 / Revised: 23 December 2019 / Accepted: 4 June 2020 © The Author(s) 2020
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/non-existence 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. Keywords Time series classification · Symbolic representation · Ensemble classification · Random Forest
Editors: Larisa Soldatova, Joaquin Vanschoren.
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Zahraa S. Abdallah [email protected] Mohamed Medhat Gaber [email protected]
1
School of Computing and Digital Technology, Birmingham City University, Birmingham, England, UK
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Machine Learning
1 Introduction Time series classification (TSC) became a topic of great interest in the last few years. Accurate classification of time series can contribute to a variety of problems in a wide range of domains such as signal processing, pattern recognition, spectrum analysis, energy consumption analysis and many others. Notable algorithms have been developed to address the classification problem, while the vast majority of research has focused on developing similarity measures for accurate classification. A significant challenge that faces time series classification is the diversity of data that reflects the diversity of domains from-where data has been collected. Time series of an electrocardiogram (ECG) in the
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