Learning the bias of a classifier in a GA-Based inductive learning environment

Authors:
Yeongjoon Kim;Chuleui Hong
Affiliations:
Software School, Sangmyung University, Seoul, Korea;Software School, Sangmyung University, Seoul, Korea
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

We have explored a meta-learning approach to improve the prediction accuracy of a classification system. In the meta-learning approach, a meta-classifier that learns the bias of a classifier is obtained so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classification system. The paper discusses our meta-learning approach in details and presents some empirical results that show the improvement we can achieve with the meta-learning approach in a GA-based inductive learning environment.