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Information journal (ISSN 2078-2489), "Artificial Intelligence" section

Special Issue "Emerging Trends and Challenges in Supervised Learning Tasks"
https://www.mdpi.com/journal/information/special_issues/Supervised_Learning_Tasks

Deadline for manuscript submissions: 31 March 2021

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Dear Colleagues,

The adoption of data mining and machine learning methods has grown exponentially in recent years, 
with an ever-increasing number of reported applications. Despite the remarkable and rapid progress in this field, 
the complexity of real-world data poses significant challenges for both researchers and practitioners. 
In the context of supervised learning tasks, the quality of the models built from real data may strongly depend 
on several factors, such as data dimensionality, the number of patterns available for training, 
the number of problem classes, the level of class imbalance, and the variability of the concepts in time. 
Further, we often encounter datasets that are affected by data quality problems, such as incompleteness or noise. 
While new and more sophisticated learning approaches are constantly being explored, 
many questions remain unanswered about their large-scale applicability and utility in real-world scenarios. 
The aim of this Special Issue is to bring together contributions that discuss problems and solutions in this area, 
especially from an application-oriented perspective, with a main emphasis on advanced supervised methods 
for learning and gaining knowledge from complex data.

Topics of interest include but are not limited to:

- Data pre-processing for supervised learning tasks;

- Dimensionality reduction and feature selection techniques;

- Learning from high-dimensional data;

- Learning from imbalanced data;

- Learning from data streams and IoT data;

- Learning in the presence of concept drift;

- Data quality issues in supervised learning;

- Noise robustness of learning algorithms;

- Issues in model evaluation and selection;

- Cost-sensitive learning;

- Ensemble learning;

- Deep learning;

- Case studies and real-world applications.

Author guidelines and submission information can be found at 
https://www.mdpi.com/journal/information/special_issues/Supervised_Learning_Tasks
Manuscripts can be submitted until the deadline. Accepted papers will be published continuously 
in the journal (as soon as accepted) and will be listed together on the special issue website.

Guest Editor
Barbara Pes (pes@unica.it)
University of Cagliari, Italy