Dear Colleagues,

Machine learning (ML), data mining (DM), and data sciences in general are among the most exciting and rapidly growing research fields today. In recent years, ML and DM have been successfully used to solve practical problems in various domains, including engineering, healthcare, medicine, manufacturing, transportation, and finance.

In this era of big data, considerable research is being focused on designing efficient ML and DM methods. Nonetheless, practical applications of ML face several challenges, such as dealing with either too small or big data, missing and uncertain data, highly multidimensional data, and the need for interpretable ML models that can provide trustable evidence and explanations of the predictions they make. Moreover, in a time where the complexity of systems is continuously growing, it becomes not always feasible to collect clean and exhaustive datasets and produce high-quality labels. In addition, most systems generate data that are subject to change over time due to external conditions resulting in non-stationary data distributions. Therefore, there is a need to do more “knowledge creation”: to develop ML and DM methods that sift through large amounts of streaming data and extract useful high-level knowledge from there, without human supervision or with very little of it. In addition, learning and obtaining good generalization from fewer training examples, efficient data/knowledge representation schemes, knowledge transfer between tasks and domains, and learning to adapt to varying contexts are also examples of important research problems.

To address such problems, this Special Issue invites researchers to contribute new methods and to demonstrate the applicability of existing methods in various fields.

Topics of interest for this Special Issue include but are not limited to the following:

Novel methods and algorithms in machine learning, data mining, data science, including data cleaning, clustering, classification, feature selection and extraction, neural networks and deep learning, representation learning, knowledge discovery, anomaly detection, fault detection, transfer learning, and active learning;
Solutions improving the state-of-the-art regarding important challenges such as big data, streaming data, time series, interactive learning, concept drift and nonstationary data, change detection, and dimensionality reduction;
Applications in various domains, for example, activity and event recognition, computational biology and bioinformatics, computational social science, game playing, healthcare, information retrieval, natural language processing, predictive maintenance, recommender systems, signal processing, web applications, and internet data;
Societal challenges associated with AI, such as fairness, accountability, and transparency or privacy, anonymity, and security.

Prof. Sławomir Nowaczyk
Dr. Mohamed-Rafik Bouguelia‬
Dr. Hadi Fanaee
Guest Editors