Building a Multi-Classifier System in a GA-Based Learning Environment

Yeong Joon Kim; Chu Leui Hong

doi:10.4028/www.scientific.net/AMM.284-287.3056

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 284-287Building a Multi-Classifier System in a GA-Based...

Building a Multi-Classifier System in a GA-Based Learning Environment

Abstract:

We have explored an approach for building a multi-classifier system in a GA-based inductive learning environment. In our approach, multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The role of a general classifier is to perform regular classification task and that of a meta-classifier is to evaluate the classification result of its general classifier and decide whether the base classifier participates into a final decision-making process or not. The paper discusses our approach in details and presents some empirical results that show the improvement we can achieve with our approach.

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Periodical:

Applied Mechanics and Materials (Volumes 284-287)

Pages:

3056-3059

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.284-287.3056

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Online since:

January 2013

Authors:

Yeong Joon Kim, Chu Leui Hong

Keywords:

Genetic Algorithm (GA), Inductive Learning, Meta-Classifier, Multi-Classifier

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References

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