Machine-Learning-Based Feature Extraction and Selection
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 13699
Special Issue Editor
Interests: text mining; artificial intelligence; image processing machine learning; deep learning; big data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The technological advances attained during the last decade, together with the enhancement of data storage and computation capabilities, have stimulated the continuous generation and storage of large volumes of high-dimensional heterogeneous data at an unprecedented speed.
In this context, feature extraction and selection methods have become a crucial mechanism to alleviate two key issues related to high-dimensional data: (i) the increase in computational efforts required for its processing and/or analysis, and (ii) the existence of additional duplicated and/or meaningless information associated with the curse of dimensionality phenomenon.
In this Special Issue, we will explore the potential of applying Machine-Learning-Based Feature Extraction and Selection methods to reduce model complexity by decreasing data dimensionality. This Special Issue is open for the publication of experimental works, properly validated designs, theoretical studies, and state-of-the-art review papers.
Dr. David Ruano Ordás
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.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.
Keywords
- information retrieval and text mining
- machine learning
- data mining and knowledge discovery
- deep learning
- information extraction
- machine learning for NLP
- dimensionality reduction
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.