A multi-agent metaheuristic hybridization to the automatic design of ensemble systems

Neto, Antonino A. Feitosa; Canuto, Anne M. P.; Xavier-Júnior, João C.; Barreto, Cephas Alves da Silveira

doi:10.1109/ijcnn.2017.7966356

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…The same system allows, in case of a large neighbourhood search, the problem decomposition into sub-problems, assigned each to an independent agent in a system. In [5], authors apply an hybridization of metaheuristics to feature and member selection within ensemble systems. Authors use a set of agents in order to optimize simultaneously the feature and base classifiers (members) of the ensemble generation process, corresponding to the problem of automatic design of Ensemble Systems.…”

Section: Introductionmentioning

confidence: 99%

Generic agent-based optimization framework to solve combinatorial problems

Ajmi¹,

Zgaya²,

Othman³

et al. 2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Section: Introductionmentioning

confidence: 99%

Generic agent-based optimization framework to solve combinatorial problems

Ajmi¹,

Zgaya²,

Othman³

et al. 2020

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

“…In [14], our first attempt to use one hybrid architecture (MAMH) for the automatic design of ensemble systems (features and base classifiers), when optimizing the classification error and good and bad diversity, is presented. However, in [14], we only analyzed these three objectives separately, in the mono-objective context. In this paper, we extend the work done in [14], using one more hybrid architecture and performing a comparative analysis with traditional population and trajectory-based metaheuristics.…”

Section: Introductionmentioning

confidence: 99%

“…However, in [14], we only analyzed these three objectives separately, in the mono-objective context. In this paper, we extend the work done in [14], using one more hybrid architecture and performing a comparative analysis with traditional population and trajectory-based metaheuristics. In addition, an empirical analysis with all different combinations of error rate, good diversity and bad diversity as optimization objectives is presented.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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.

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A multi-agent metaheuristic hybridization to the automatic design of ensemble systems

Cited by 2 publications

References 25 publications

Generic agent-based optimization framework to solve combinatorial problems

Generic agent-based optimization framework to solve combinatorial problems

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

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