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Monotonicity Detection and Enforcement in Longitudinal Classification

Ovchinnik, Sergey, Otero, Fernando E.B., Freitas, Alex A. (2019) Monotonicity Detection and Enforcement in Longitudinal Classification. In: Bramer, Max and Petridis, Miltos, eds. Lecture Notes in Artificial Intelligence. Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019 Cambridge, UK, December 17–19, 2019 Proceedings. 11927. Springer ISBN 978-3-030-34884-7. (doi:10.1007/978-3-030-34885-4_5) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:77372)

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http://dx.doi.org/10.1007/978-3-030-34885-4_5

Abstract

Longitudinal datasets contain repeated measurements of the same variables at different points in time, which can be used by researchers to discover useful knowledge based on the changes of the data over time. Monotonic relations often occur in real-world data and need to be preserved in data mining models in order for the models to be acceptable by users. We propose a new methodology for detecting monotonic relations in longitudinal datasets and applying them in longitudinal classification model construction. Two different approaches were used to detect monotonic relations and include them into the classification task. The proposed approaches are evaluated using data from the English Lon- gitudinal Study of Ageing (ELSA) with 10 different age-related diseases used as class variables to be predicted. A gradient boosting algorithm (XGBoost) is used for constructing classification models in two scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a positive impact on model acceptance in real world applications.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-34885-4_5
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 13 Oct 2019 21:44 UTC
Last Modified: 04 Feb 2020 04:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77372 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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