Investigating the Use of an Agent-Based Multi-classifier System for Classification Tasks

Canuto, Anne M. P.; Santos, Antonio Marques dos; Abreu, Márjory Da Costa; Bezerra, Valéria Maria S.; Souza, Fernanda Maria de; Gomes, Manuel F.

doi:10.1007/978-3-540-30499-9_131

Cited by 10 publications

(15 citation statements)

References 6 publications

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“…As already mentioned, this system has been successfully used in some conventional tasks, such as [11,12], and it has been shown to be very efficient with tasks with feature distribution as well [13]. In these tasks, ClassAge has shown to be able to minimize the information loss when the agents do not have access to all information about the input pattern.…”

Section: The Classage Systemmentioning

confidence: 94%

“…This system is composed of several classifier agents which negotiate a common output (among the agents) for the test patterns. This system has shown a satisfactory improvement in performance, when compared with ensembles [11,12,13].…”

Section: Introductionmentioning

confidence: 94%

“…The agent-based system proposed in [7,12], known as ClassAge, can be considered as an alternative to transform the centralized decision making process of a multiclassifier system into a distributed, flexible and incremental one. This system is composed of a set of classifier agents that receive an input pattern and gives its label class.…”

Section: The Classage Systemmentioning

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: The Classage Systemmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 94%

Section: The Classage Systemmentioning

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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“…As a result of this, the NeurAge system was proposed in [7]. This system is composed of several neural agents who communicate among themselves in order to reach a common result to an input pattern.…”

Section: Introductionmentioning

confidence: 99%

“…The NeurAge system has been successfully used in some conventional classification tasks, such as in [5,7]. In order to perform an extensive investigation of the NeurAge system, it is interesting to analyze its performance in other situations, such as: using data distribution among the agents (distributed classification tasks).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Data Distribution Methods in an Agent-Based Neural System for Classification Tasks

Santana

Canuto

Xavier-Júnior

et al. 2006

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)

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The NeurAge (Neural agents) system has been proposed as an alternative to transform the centralized decision making process of a multi-classifier system into a distributed, flexible and incremental one. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features might be distributed over a set of agent. These applications can be classified as distributed classification tasks. In this paper, a comparative investigation of the NeurAge system using some methods for data distribution will be performed. In addition, the performance of the NeurAge system will be compared with some existing multi-classifier systems.

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Combining Multiagent Negotiation and an Interacting Verification Process to Enhance Biometric-Based Identification

Abreu

Fairhurst

2011

Lecture Notes in Computer Science

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Investigating the Use of an Agent-Based Multi-classifier System for Classification Tasks

Cited by 10 publications

References 6 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

A Comparative Analysis of Data Distribution Methods in an Agent-Based Neural System for Classification Tasks

Combining Multiagent Negotiation and an Interacting Verification Process to Enhance Biometric-Based Identification

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