Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

作者: Dariusz Brzezinski , Jerzy Stefanowski

DOI: 10.1109/TNNLS.2013.2251352

关键词:

摘要: … algorithm, called Accuracy Updated Ensemble, which should react to different types of concept drift much better than related adaptive ensembles… for blockbased algorithms, while adding …

参考文章(41)
Marcus A. Maloof, J. Zico Kolter, Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts Journal of Machine Learning Research. ,vol. 8, pp. 2755- 2790 ,(2007) , 10.5555/1314498.1390333
Eduardo Jaques Spinosa, João Gama, Pedro Pereira Rodrigues, André Carlos Ponce de Leon Ferreira de Carvalho, Knowledge Discovery from Data Streams. Web Intelligence and Security - Advances in Data and Text Mining Techniques for Detecting and Preventing Terrorist Activities on the Web. pp. 125- 138 ,(2010)
Ludmila I. Kuncheva, Combining Pattern Classifiers John Wiley & Sons, Inc.. ,(2004) , 10.1002/0471660264
Kevin Bache, Moshe Lichman, UCI Machine Learning Repository University of California, School of Information and Computer Science. ,(2007)
Ludmila I. Kuncheva, Classifier Ensembles for Changing Environments multiple classifier systems. pp. 1- 15 ,(2004) , 10.1007/978-3-540-25966-4_1
Albert Bifet, Rafael Morales-Bueno, Ricard Gavald, Manuel Baena-Garc, Jose del Campo ¶ Avila, Early Drift Detection Method ,(2005)
Philip S. Yu, Wei Fan, Haixun Wang, Yi an Huang, Active mining of data streams siam international conference on data mining. pp. 457- 461 ,(2004)
Indrė Žliobaitė, Combining Time and Space Similarity for Small Size Learning under Concept Drift ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems. pp. 412- 421 ,(2009) , 10.1007/978-3-642-04125-9_44