An ensemble of autonomous auto-encoders for human activity recognition

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Kemilly Dearo Garcia (Autor) Outra; Cláudio Rebelo de Sá (Autor) Outra; Mannes Poel (Autor) Outra; Tiago Carvalho (Autor) Outra; João Mendes Moreira (Autor) FEUP; João M. P. Cardoso (Autor) FEUP; André C. P. L. F. de Carvalho (Autor) Outra; Joost N. Kok (Autor) Outra

Revista

Título: Neurocomputing

Vol. 439

Páginas: 271-280

ISSN: 0925-2312

Editora: Elsevier

Indexação

ISI Web of Knowledge - 29 Citações

Scopus - 39 Citações

Outras Informações

ID Authenticus: P-00T-FF1

DOI: 10.1016/j.neucom.2020.01.125

Abstract (EN): Human Activity Recognition is focused on the use of sensing technology to classify human activities and to infer human behavior. While traditional machine learning approaches use hand-crafted features to train their models, recent advancements in neural networks allow for automatic feature extraction. Auto-encoders are a type of neural network that can learn complex representations of the data and are commonly used for anomaly detection. In this work we propose a novel multi-class algorithm which consists of an ensemble of auto-encoders where each auto-encoder is associated with a unique class. We compared the proposed approach with other state-of-the-art approaches in the context of human activity recognition. Experimental results show that ensembles of auto-encoders can be efficient, robust and competitive. Moreover, this modular classifier structure allows for more flexible models. For example, the extension of the number of classes, by the inclusion of new auto-encoders, without the necessity to retrain the whole model. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 10