Artículo
Decomposition methods for machine learning with small, incomplete or noisy datasets
Fecha de publicación:
11/2020
Editorial:
MDPI
Revista:
Applied Sciences
ISSN:
2076-3417
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
In many machine learning applications, measurements are sometimes incomplete or noisy resulting in missing features. In other cases, and for different reasons, the datasets are originally small, and therefore, more data samples are required to derive useful supervised or unsupervised classification methods. Correct handling of incomplete, noisy or small datasets in machine learning is a fundamental and classic challenge. In this article, we provide a unified review of recently proposed methods based on signal decomposition for missing features imputation (data completion), classification of noisy samples and artificial generation of new data samples (data augmentation). We illustrate the application of these signal decomposition methods in diverse selected practical machine learning examples including: brain computer interface, epileptic intracranial electroencephalogram signals classification, face recognition/verification and water networks data analysis. We show that a signal decomposition approach can provide valuable tools to improve machine learning performance with low quality datasets.
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Articulos de INST.ARG.DE RADIOASTRONOMIA (I)
Articulos de INST.ARG.DE RADIOASTRONOMIA (I)
Citación
Caiafa, César Federico; Sole Casals, Jordi; Marti Puig, Pere; Sun, Zhe; Tanaka,Toshihisa; Decomposition methods for machine learning with small, incomplete or noisy datasets; MDPI; Applied Sciences; 10; 23; 11-2020; 1-21
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