Machine Learning Methods with Noisy, Incomplete or Small Datasets
Source abstract
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.
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Research context
OpenAlex topics: Machine Learning and Data Classification · Anomaly Detection Techniques and Applications · Imbalanced Data Classification Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Instituto Argentino de Radioastronomía—CCT La Plata, CONICET/CIC-PBA/UNLP, 1894 V. Elisa, Argentina
Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi 351-0198, Japan
Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan
Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Barcelona, Spain
References (15)
Show source-provided references
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