Special Issue "Classifier Ensembles: Efficient Techniques to Define Robust System Structures"

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: 1 July 2019

Special Issue Editor

Guest Editor
Dr. Anne Canuto

Department of Informatics and Applied Mathematics (DIMAp), Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, Brazil
Website | E-Mail
Interests: classifier ensemble; optimization techniques; deep learning; multi-label classification

Special Issue Information

Dear Colleagues,

One of the main challenges in the design of efficient classifier ensembles is the process of selecting their structure, especially with regard to individual classifiers with their respective parameters as well as combination methods. One way to improve the performance of classifier ensembles is by automatically selecting the best classifiers and combination method to compose their structure. This selection can be done statically or dynamically. In the static selection, once the ensemble structure is defined, all test instances will be classified by the same structure. In other words, the ensemble structure is selected before starting the classification step and used for all instances of this step. This selection is the most traditional and this problem has been treated as a meta-learning problem, automatic selection of machine learning (auto-ML) or an optimization problem. Recently, based on the assumption that every test instance has particularities and different levels of difficulties in the classification process, the dynamic selection approach has arisen. In this selection, each instance is classified by a different ensemble structure (set of individual classifiers and method of combination). Thus, in order to classify N instances it is necessary to set N ensemble structures, one for each test instance, selecting the most appropriate structure to classify one specific instance. In dynamic selection, the selection of the ensemble structure is performed during the testing phase.

Despite the high number of studies and techniques, finding an optimal parameter set that maximizes classification accuracy of an ensemble system is still an open problem. The search space for all parameters of an ensemble system (classifier type, size, classifier parameters, combination method and feature selection) is very large. This Special Issue on “Classifier Ensembles: Efficient Techniques to Define Robust System Structures” aims to promote new theories, techniques, and methods with which to exploit the selection of efficient ensemble structures, including both selection approaches, static and dynamic selections.

Dr. Anne Canuto
Guest Editor

Manuscript Submission Information

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Keywords

  • Classifier Ensembles
  • Optimization Techniques
  • Meta-learning
  • Dynamic Selection in Classifier Ensembles

Published Papers

This special issue is now open for submission.
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