About: Constructed temporal features for longitudinal classification of human ageing data

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91011 Constructed temporal features for longitudinal classification of human ageing data
sameAs	Constructed temporal features for longitudinal classification of human ageing data
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dc:hasVersion	Constructed temporal features for longitudinal classification of human ageing data (PDF)
Title	Constructed temporal features for longitudinal classification of human ageing data
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-10-17
Creator	A. Freitas Caio Ribeiro
status	peer reviewed published
Publisher	IEEE
abstract	Standard classification algorithms ignore the time-related information contained in longitudinal data, as they do not consider the time indexes of the features’ different measurements. Accounting for temporal patterns may improve the algorithms’ performance, when applied to longitudinal data. Representing temporal patterns in the data itself has the advantage that those patterns are generic enough to be used with existing powerful classification algorithms, without requiring the design of new and more complex algorithms to exploit them. In this article, we propose 6 different types of constructed temporal features (3 of them being novel contributions), calculated from the values of the different feature measurements taken over time, and investigate whether adding those constructed temporal features to the original longitudinal dataset improves the classification model’s predictive accuracy. Our experiments involved 20 real-world longitudinal datasets created from a human-ageing study, and showed that the proposed approach of adding the constructed temporal features to the original feature set produced better classifiers overall.
Is Part Of	Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI) https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/91011#authors
presented at	2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91011 Constructed temporal features for longitudinal classification of human ageing data

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