INVESTIGADORES
CAIAFA Cesar Federico
artículos
Título:
Machine Learning Methods with Noisy, Incomplete or Small Datasets
Autor/es:
CESAR F. CAIAFA; SUN ZHE; TOSHIHISA TANAKA; PERE MARTÍ-PUIG; JORDI SOLE-CASALS
Revista:
Applied Sciences
Editorial:
MDPI
Referencias:
Lugar: Basel; Año: 2021 vol. 2021
ISSN:
2076-3417
Resumen:
In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue ?Machine Learning Methods with Noisy, Incomplete or Small Datasets?, Applied Sciences (ISSN 2076-3417). These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise orartefacts. Contributions in applied sciences include medical applications, epidemic management and industrial applications, among others. We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.
