Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Genetic programming with boosting for ambiguities in regression problems
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Research issues in mining multiple data streams
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A novel approach to design classifiers using genetic programming
IEEE Transactions on Evolutionary Computation
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.