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Information 2018, 9(11), 268; https://doi.org/10.3390/info9110268

Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles

1
Department of Informatics and Applied Mathematics (DIMAp), Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, Brazil
2
Digital Metrolopis Institute—IMD, Federal University of Rio Grande do Norte (UFRN), Natal 59078-970, Brazil
*
Author to whom correspondence should be addressed.
Received: 7 September 2018 / Revised: 17 October 2018 / Accepted: 20 October 2018 / Published: 26 October 2018
(This article belongs to the Special Issue Multi-objective Evolutionary Feature Selection)
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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 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. View Full-Text
Keywords: hybrid metaheuristics; classifier ensemble; parameter selection hybrid metaheuristics; classifier ensemble; parameter selection
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Feitosa Neto, A.A.; Canuto, A.M.P.; Xavier-Junior, J.C. Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles. Information 2018, 9, 268.

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