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Nested Trees for Longitudinal Classification

Ovchinnik, Sergey, Otero, Fernando E.B., Freitas, Alex A. (2021) Nested Trees for Longitudinal Classification. In: 37th ACM/SIGAPP Symposium On Applied Computing, April 25 - April 29, 2022. (In press) (KAR id:92832)

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Longitudinal datasets contain repeated measurements of the same variables at different points in time. Longitudinal data mining algorithms aim to utilize such datasets to extract interesting knowledge and produce useful models. Many existing longitudinal classification methods either dismiss the longitudinal aspect of the data during model construction or produce complex models that are scarcely interpretable. We propose a new longitudinal classification algorithm based on decision trees, named Nested Trees. It utilizes a unique longitudinal model construction method that is fully aware of the longitudinal aspect of the predictive attributes (variables) and constructs tree nodes that make decisions based on a longitudinal attribute as a whole, considering measurements of that attribute across multiple time points. The algorithm was evaluated using 10 classification tasks based on the English Longitudinal Study of Ageing (ELSA) data.

Item Type: Conference or workshop item (Poster)
Uncontrolled keywords: Classification, Longitudinal Data, Decision Trees
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 24 Jan 2022 12:28 UTC
Last Modified: 25 Apr 2022 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
Freitas, Alex A.:
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