Abstract
The paper proposes to use gene expression programming with metagenes as a base classifier integrated with the Fisher exact test drift detector. The approach assumes maintaining during the classification process two windows, recent and older. If the drift is detected, the recent window is used to induce a new classifier with a view to adapt to the drift changes. The idea is validated in the computational experiment where the performance of the GEP-based classifier with Fisher exact test detector is compared with classifiers using Naïve Bayes and Hoeffding tree as the base learners.
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Jedrzejowicz, J., Jedrzejowicz, P. (2020). Gene Expression Programming Classifier with Concept Drift Detection Based on Fisher Exact Test. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore. https://doi.org/10.1007/978-981-13-8311-3_18
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DOI: https://doi.org/10.1007/978-981-13-8311-3_18
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