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.
This work was supported by Ministry of Education and Human Resources Development through Embedded Software Open Education Resource Center (ESC) at Sangmyung University.
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Kim, Y., Hong, C. (2005). Learning the Bias of a Classifier in a GA-Based Inductive Learning Environment. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_96
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DOI: https://doi.org/10.1007/11538059_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
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