Dear Colleagues,

In recent years, feature selection has been acknowledged as one of the significant activity research fields due to the obvious emergence of datasets comprising large numbers of features. As a result, feature selection was considered an excellent technique for both improving the modeling of the underlying data generation process and lowering the cost of obtaining the features. Additionally, from a machine learning perspective, because feature selection may shrink the complexity of an issue, it can be utilized to preserve or even boost the effectiveness of algorithms while minimizing computing costs. Recently, the emergence of Big Data has created new hurdles for machine learning researchers, who must now handle vast amounts of data, both in terms of instances and characteristics, rendering the learning process more complicated and computationally intensive than ever. While engaging with a significant number of features, the efficiency of learning algorithms might degrade due to overfitting; as learned models become increasingly complicated, their interpretability decreases, and the performance and efficacy of the algorithms are affected. Unfortunately, some of the most widely used algorithms were designed when dataset sizes were considerably smaller, and therefore do not scale well in the wake of these developments. Thus, it is necessary to repurpose these effective methods to address Big Data concerns.

For this Special Issue, we seek papers concerning current advances in feature selection algorithms for high-dimensional settings, as well as review papers that will motivate ongoing efforts to grasp the challenges commonly faced in this field. High-quality articles that address both theoretical and practical challenges relating to feature selection algorithms are welcome.

Dr. Muhammad Adnan Khan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• algorithms and techniques for feature selection based on evolutionary search
• ensemble methods for feature selection
• feature selection for high dimensional data
• feature selection for time series data
• feature selection applications
• feature selection for textual data
• deep feature selection