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Feature selection for supervised learning and compression
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Taylor, Phillip M., Griffiths, Nathan, Hall, V., Zhou, Z. and Mouzakitis, A. (2022) Feature selection for supervised learning and compression. Applied Artificial Intelligence, 36 (1). 2034293. doi:10.1080/08839514.2022.2034293 ISSN 1087-6545.
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Official URL: https://doi.org/10.1080/08839514.2022.2034293
Abstract
Supervised feature selection aims to find the signals that best predict a target variable. Typical approaches use measures of correlation or similarity, as seen in filter methods, or predictive power in learned models, as seen in wrapper methods. In both approaches, the selected features often have high entropies and are not suitable for compression. This is a particular drawback in the automotive domain where fast communication and archival of vehicle telemetry data is increasingly important, especially with technologies such as V2V and V2X (vehicle-to-vehicle and vehicle-to-everything communication). This paper aims to select features with good predictive performances and good compression by introducing a compressibility factor into several existing feature selection approaches. Where appropriate, performance guarantees are provided for greedy searches based on monotonicity and submodularity. Using the language of entropy, the relationship between relevance, redundancy, and compressibility is discussed from the perspective of signal selection. The approaches are then demonstrated in selecting features from a vehicle Controller Area Network for use in SVMs in a regression task, namely predicting fuel consumption, and a classification task, namely identifying Points of Interest. We show that while predictive performance is slightly lower when compression is considered, the compressibility of the selected features is significantly improved.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Supervised learning (Machine learning), Data compression (Computer science), Automated vehicles, Predictive control | |||||||||
Journal or Publication Title: | Applied Artificial Intelligence | |||||||||
Publisher: | Taylor & Francis Inc. | |||||||||
ISSN: | 1087-6545 | |||||||||
Official Date: | 6 March 2022 | |||||||||
Dates: |
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Volume: | 36 | |||||||||
Number: | 1 | |||||||||
Article Number: | 2034293 | |||||||||
DOI: | 10.1080/08839514.2022.2034293 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 24 January 2022 | |||||||||
Date of first compliant Open Access: | 7 March 2022 | |||||||||
RIOXX Funder/Project Grant: |
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