Special Issue "Multi-objective Evolutionary Feature Selection"
Deadline for manuscript submissions: closed (1 April 2019)
Prof. Dr. José T. Palma
In recent years, it has been shown that Multi-objective Evolutionary Algorithms are powerful techniques to solve feature selection problems. The success lies fundamentally in the suitability of the Multi-objective Evolutionary Algorithm’s ability to approximate solutions in NP-hard problems, as well as in the possibility of addressing the feature selection problem as a multi-objective optimization problem where performance is maximized and the number of selected attributes is minimized, thus reducing the complexity of the models while improving their performance.
This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in Multi-objective Evolutionary Computation techniques for Feature Selection. The scope of this Special Issue encompasses applications in Engineering, Artificial Intelligence, Physical Science, Social Science, Business, Economy, Market Research, and Medical and Health Care. Topics of interest include (but are not limited to) the following subject categories:
- Multi-objective evolutionary univariate/multivariate feature selection methods for classification/regression/clustering/association rules.
- Multi-objective evolutionary filter/wrapper/embedded feature selection methods for classification/regression/clustering/association rules.
- Multi-objective evolutionary feature selection for unbalanced data.
- Multi-objective evolutionary feature selection for multiple instance learning.
- Multi-objective evolutionary feature selection for multi-class classification.
- Multi-objective evolutionary feature selection for fuzzy classification.
- Multi-objective evolutionary feature selection for text classification.
- Multi-objective evolutionary feature selection for time-series forecasting.
- New representations and variation operators for multi-objective evolutionary feature selection.
- Multi-objective evolutionary feature selection with many objectives.
- Multi-objective differential evolution feature selection.
- Decision making in multi-objective evolutionary feature selection.
- New performance metrics for multi-objective evolutionary feature selection.
- Multi-objective evolutionary instance/feature selection.
- Multi-objective evolutionary feature selection for big data.
- Parallel multi-objective evolutionary feature selection.
- Distributed multi-objective evolutionary feature selection.
Prof. Fernando Jiménez
Prof. Dr. José T. Palma
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 papers will be 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. Information is an international peer-reviewed open access monthly 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 1000 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.
- Evolutionary Algorithms
- Multi-Objective Optimization
- Feature Selection
- Association Rules