Using an Evolutionary Agent-Based System for Classification Tasks

Oliveira, Diogo F. de; Canuto, Anne M. P.; Souto, Michael Vandesteen Silva

doi:10.1109/isda.2007.98

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…As it can be observed from Table II, the original ClassAge system surpasses the accuracy of the best classifiers, for all three system sizes. It is an interesting result, because although the base classifiers have provided an overall poor performance, the accuracy of the ClassAge can be considered reasonable, when compared with other combination systems [14].…”

Section: Results Analysismentioning

confidence: 98%

“…The first one is the inclusion of a genetic algorithm in order to optimize the functioning of this system. In [14], an initial investigation was conducted in order to add a genetic algorithm in the functioning of the ClassAge system. However, in this paper, a more extensive investigation is performed, using a genetic algorithm to select optimal classifier parameters during the functioning of the ClassAge system.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Use of an Evolutionary Agent-based System for Classification Tasks

Oliveira

Canuto

Souto

2007

2007 International Joint Conference on Neural Networks

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The idea of including intelligent agents in the structure of multi-classifier systems has emerged in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the ClassAge system was proposed. This system has presented good results in some classification tasks. In this paper, an extension of this system is presented. Basically, an optimization technique is used to optimize the functioning of ClassAge. Also, each ClassAge agent is composed of a group of classifiers, instead of one classifier that was used in the original version.

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Section: Results Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Investigating the Use of an Evolutionary Agent-based System for Classification Tasks

Oliveira

Canuto

Souto

2007

2007 International Joint Conference on Neural Networks

Self Cite

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“…More guidelines for deciding about the adoption of agents and MAS can be seen in Bogg et al (2008), agentbuilder, Oliveira et al (1999b), Oliveira (1999a), and Jennings et al (1998and Jennings et al ( , 1995. The decision of using an outer adaptation architecture can be taken according to the physical and computational nature of the application domain.…”

Section: Decision On the Adoption Of Noshapementioning

confidence: 99%

Realizing the NOSHAPE MAS Organizational Model

Abbas

2015

International Journal of Agent Technologies and Systems

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Dynamic reorganization of multi-agent systems (MAS) is currently an interesting and active research area. New emerging concepts such as self-organization and emergence are also getting great focus by MAS research community because of their captivating benefits for engineering complex large-scale MAS applications. Dynamic reorganization is considered as an effective way for building adaptive MAS. This paper provides an operational view of the conceptually proposed NOSHAPE dynamic organizational model for dynamically reorganized multi-agent systems (Abbas, 2014). The operational view concerns possible design decisions, implementation issues, and suggested usage of the NOSHAPE model for practically engineering real life applications. This paper also identifies the possible solutions to be applied throughout the system design to resolve these concerns.

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Using an Evolutionary Agent-Based System for Classification Tasks

Cited by 2 publications

References 19 publications

Investigating the Use of an Evolutionary Agent-based System for Classification Tasks

Investigating the Use of an Evolutionary Agent-based System for Classification Tasks

Realizing the NOSHAPE MAS Organizational Model

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