Genetic Programming is an evolutionary soft computing approach. Data streams are the order of the day input sources. In general, data streams exhibit a peculiar behavior of drifting the concepts as time passes by. Here is a study of GP Classifier on Concept Drifting Data Streams. GP classifier performance is compared to that of other state-of-the-art data mining and stream classification approaches. Boosting is a machine learning meta-algorithm for performing supervised learning. A weak learner is defined to be a classifier which is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification. Boosting combines a set of weak learners to create a strong learner. It is observed that the Boosting GP approach is beating Boosting Naïve Bayes classification on Concept Drifting Data Streams. Hence it is found that GP is a competent algorithm for Concept Drifting Data Stream classification. © 2012 Springer-Verlag GmbH Berlin Heidelberg.
CITATION STYLE
Nagendra Kumar, D. J., Murthy, J. V. R., Satapathy, S. C., & Pullela, S. V. V. S. R. K. (2012). GP boosting classification on concept drifting data streams. In Advances in Intelligent and Soft Computing (Vol. 132 AISC, pp. 265–272). Springer Verlag. https://doi.org/10.1007/978-3-642-27443-5_30
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