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Special Issue "Recent Advances in Feature Selection"
A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).
Deadline for manuscript submissions: 15 September 2023 | Viewed by 13295
Special Issue Editors
Interests: pattern recognition; machine learning; data mining
Interests: machine learning; deep learning; feature selection
Special Issue Information
Feature selection is of immense importance for the efficient design and development of all pattern recognition and data mining applications. Research in this area has a long history, and many techniques are already available. Recently, with the development of the internet, social media, sensors and communication technologies, the rapid growth of high dimensional data has enhanced the demand for sophisticated machine learning (ML) tools due to their fast and efficient processing. Feature selection is known to improve the performance of machine learning models by reducing the dimensionality of data and computational cost.
The major advancement of deep learning techniques in the area of ML to date has created the opportunity to develop new methods of data representation and feature selection suitable for dealing with deep neural network (DNN) models. DNN models are known for their capability of implicit feature extraction from raw data (especially image and video data) during the process of recognition, which is helpful in some applications. However, knowledge of the explicit relation between the features of the input data and the output classes is very important in many practical applications, such as in healthcare, bioinformatics, banking and finance, telecommunication and decision support systems. Extraction and selection of interpretable features have a great effect on the scientific basis and the performance of a vast majority of real-world AI applications.
This Special Issue calls for contributions from researchers that target the recent developments in the field of feature selection associated with machine learning, including deep learning models for a wide variety of data from both theoretical and practical perspectives.
The topics of interest include, but are not limited to, the following:
New feature selection algorithms;
Evolutionary search-based techniques for feature selection;
Clustering and graph-based techniques for feature selection;
Feature selection for high dimensional data;
Feature selection for time series data ;
Feature selection for textual data;
Feature selection for DNN models;
Deep feature selection;
Ensemble methods for feature selection;
Feature selection applications
Prof. Dr. Basabi Chakraborty
Dr. Saptarsi Goswami
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. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly 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 1400 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.
- feature selection
- feature subset selection
- ensemble feature selection
- dimensionality reduction
- interpretable feature
- deep learning