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http://dx.doi.org/10.6109/jkiice.2015.19.1.35

A Meta-learning Approach for Building Multi-classifier Systems in a GA-based Inductive Learning Environment  

Kim, Yeong-Joon (Department of Computer Science, Sangmyung University)
Hong, Chul-Eui (Department of Computer Science, Sangmyung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.1, 2015 , pp. 35-40 More about this Journal
Abstract
The paper proposes a meta-learning approach for building multi-classifier systems in a GA-based inductive learning environment. In our meta-learning approach, a classifier consists of a general classifier and a meta-classifier. We obtain a meta-classifier from classification results of its general classifier by applying a learning algorithm to them. The role of the meta-classifier is to evaluate the classification result of its general classifier and decide whether to participate into a final decision-making process or not. The classification system draws a decision by combining classification results that are evaluated as correct ones by meta-classifiers. We present empirical results that evaluate the effect of our meta-learning approach on the performance of multi-classifier systems.
Keywords
Genetic Algorithms; Inductive Learning; Meta-learning Approach; Multi-classifier Systems;
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