Capítulo de Libro
Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
Título del libro: Machine Learning Methods with Noisy, Incomplete or Small Datasets
Fecha de publicación:
2021
Editorial:
MDPI
ISBN:
978-3-0365-1288-4
Idioma:
Inglés
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.
Palabras clave:
machine learning
,
incomplete data
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Capítulos de libros de INST.ARG.DE RADIOASTRONOMIA (I)
Capítulos de libros de INST.ARG.DE RADIOASTRONOMIA (I)
Citación
Caiafa, César Federico; Solé Casals, Jordi; Marti Puig, Pere; Zhe, Sun ; Tanaka, Toshihisa ; Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets; MDPI; 2021; 5-24
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