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

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. 키워드 : 유전 알고리즘, 귀납적 학습, 메타 학습법, 다중 분류기 시스템 Key word : Genetic Algorithms, Inductive Learning, Meta-learning Approach, Multi-classifier SystemsJournal of the Korea Institute of Information andCommunication Engineering