A Study of Decision Tree Induction for Data Stream Mining Using Boosting Genetic Programming Classifier
| Autor: | J. V. R. Murthy, Dirisala J. Nagendra Kumar, Suresh Chandra Satapathy, S. V. V. S. R. Kumar Pullela |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Boosting (machine learning)
Computer science business.industry Data stream mining Pattern recognition Quadratic classifier Machine learning computer.software_genre BrownBoost Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION LPBoost Margin classifier Gradient boosting Artificial intelligence business computer |
| Zdroj: | Swarm, Evolutionary, and Memetic Computing ISBN: 9783642271717 SEMCCO (1) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|