A Study of Decision Tree Induction for Data Stream Mining Using Boosting Genetic Programming Classifier

摘要

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 Naive Bayes classification. Hence it is found that GP is a competent algorithm for Data Stream classification.