Abstract
Genetic Programming is an evolutionary soft computing approach. Data streams are the order of the day input mechanisms. Here is a study of GP Classifier on Data Streams. GP classification 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 (it can label examples better than random guessing). 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. Hence it is found that GP is a competent algorithm for Data Stream classification.
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Kumar, D.J.N., Murthy, J.V.R., Satapathy, S.C., Pullela, S.V.V.S.R.K. (2011). A Study of Decision Tree Induction for Data Stream Mining Using Boosting Genetic Programming Classifier. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_39
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DOI: https://doi.org/10.1007/978-3-642-27172-4_39
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