Machine learning methods with noisy, incomplete or small datasets

16Citations
Citations of this article
81Readers
Mendeley users who have this article in their library.

Abstract

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 or artefacts. Contributions in applied sciences include medical applications, epidemic management tools, methodological work, 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.

Cite

CITATION STYLE

APA

Caiafa, C. F., Sun, Z., Tanaka, T., Marti-Puig, P., & Solé-Casals, J. (2021). Machine learning methods with noisy, incomplete or small datasets. Applied Sciences (Switzerland), 11(9). https://doi.org/10.3390/app11094132

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free