Machine Learning Methods with Noisy, Incomplete or Small Datasets

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 Noisy, Incomplete Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide variety approaches to real-world problems where available datasets suffer from imperfections such as missing values, noise artefacts. Contributions applied sciences include medical applications, epidemic management tools, methodological work, and industrial among others. We believe that will bring new ideas solving challenging problem, clear examples application scenarios.