Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58378
Título: Interval incremental learning of interval data streams and application to vehicle tracking
Palavras-chave: Granular machine learning
Online learning
Granular computing
Interval analysis
Data stream
Data do documento: 2023
Editor: Elsevier
Citação: LEITE, D. et al. Interval incremental learning of interval data streams and application to vehicle tracking. Information Sciences, [S.l.], v. 630, p. 1-22, June 2023.
Resumo: This paper presents a method called Interval Incremental Learning (IIL) to capture spatial and temporal patterns in uncertain data streams. The patterns are represented by information granules and a granular rule base with the purpose of developing explainable human-centered computational models of virtual and physical systems. Fundamentally, interval data are either included into wider and more meaningful information granules recursively, or used for structural adaptation of the rule base. An Uncertainty-Weighted Recursive-Least-Squares (UW-RLS) method is proposed to update affine local functions associated with the rules. Online recursive procedures that build interval-based models from scratch and guarantee balanced information granularity are described. The procedures assure stable and understandable rule-based modeling. In general, the model can play the role of a predictor, a controller, or a classifier, with online sample-per-sample structural adaptation and parameter estimation done concurrently. The IIL method is aligned with issues and needs of the Internet of Things, Big Data processing, and eXplainable Artificial Intelligence. An application example concerning real-time land-vehicle localization and tracking in an uncertain environment illustrates the usefulness of the method. We also provide the Driving Through Manhattan interval dataset to foster future investigation.
URI: https://www.sciencedirect.com/science/article/pii/S0020025523002165
http://repositorio.ufla.br/jspui/handle/1/58378
Aparece nas coleções:DEG - Artigos publicados em periódicos

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