Special Issue "Multi-objective Evolutionary Feature Selection"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (1 April 2019)

Special Issue Editors

Guest Editor
Dr. Fernando Jiménez

Faculty of Informatics, University of Murcia, Murcia, Spain
Website | E-Mail
Interests: Evolutionary Algorithms; Multi-Objective Optimization; Fuzzy Classification; Feature Selection; Big Data
Guest Editor
Prof. Dr. José T. Palma

Faculty of Informatics, Department of Information and Communications Engineering, University of Murcia, Murcia, Spain
Website | E-Mail
Interests: intelligent data analysis; knowledge-based; ambient intelligent applications; fuzzy logic; ontologies; temporal reasoning

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • Evolutionary Algorithms
  • Multi-Objective Optimization
  • Feature Selection
  • Classification
  • Regression
  • Clustering
  • Association Rules

Published Papers (1 paper)

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Research

Open AccessArticle Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles
Information 2018, 9(11), 268; https://doi.org/10.3390/info9110268
Received: 7 September 2018 / Revised: 17 October 2018 / Accepted: 20 October 2018 / Published: 26 October 2018
PDF Full-text (330 KB) | HTML Full-text | XML Full-text
Abstract
Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining [...] Read more.
Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining metaheuristics in an efficient way has emerged recently, in a field called hybridization of metaheuristics or, simply, hybrid metaheuristics. As a result of this, hybrid metaheuristics can be successfully applied in different optimization problems. In this paper, two hybrid metaheuristics, MAMH (Multiagent Metaheuristic Hybridization) and MAGMA (Multiagent Metaheuristic Architecture), are adapted to be applied in the automatic design of ensemble systems, in both mono- and multi-objective versions. To validate the feasibility of these hybrid techniques, we conducted an empirical investigation, performing a comparative analysis between them and traditional metaheuristics as well as existing existing ensemble generation methods. Our findings demonstrate a competitive performance of both techniques, in which a hybrid technique provided the lowest error rate for most of the analyzed objective functions. Full article
(This article belongs to the Special Issue Multi-objective Evolutionary Feature Selection)
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