A Comparison of Ensemble and Case-Base Maintenance Techniques for Handling Concept Drift in Spam Filtering

This research was supported by funding from Enterprise Ireland under grant no. CFTD/03/219 and funding from Science Foundation Ireland under grant no. SFI-02IN.1I111

[1]  G DietterichThomas.  Approximate statistical tests for comparing supervised classification learning algorithms , 1998 .

[2]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[3]  Ludmila I. Kuncheva,et al.  Classifier Ensembles for Changing Environments , 2004, Multiple Classifier Systems.

[4]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[5]  Leo Breiman,et al.  Pasting Small Votes for Classification in Large Databases and On-Line , 1999, Machine Learning.

[6]  KlinkenbergRalf.  Learning drifting concepts: Example selection vs. example weighting , 2004 .

[7]  Gerhard Widmer,et al.  Adapting to Drift in Continuous Domains , 2007 .

[8]  Marcus A. Maloof,et al.  Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.

[9]  Kenneth O. Stanley.  Learning Concept Drift with a Committee of Decision Trees , 2003 .

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Marcos Salganicoff,et al.  Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching , 1997, Artificial Intelligence Review.

[12]  Alexey Tsymbal,et al.  Bagging and Boosting with Dynamic Integration of Classifiers , 2000, PKDD.

[13]  Padraig Cunningham,et al.  An Assessment of Case-Based Reasoning for Spam Filtering , 2005, Artificial Intelligence Review.

[14]  Ralf Klinkenberg,et al.  Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..

[15]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[16]  Padraig Cunningham,et al.  A case-based technique for tracking concept drift in spam filtering , 2004, Knowl. Based Syst..

[17]  Padraig Cunningham,et al.  An Analysis of Case-Base Editing in a Spam Filtering System , 2004, ECCBR.

[18]  Niall M. Adams,et al.  The impact of changing populations on classifier performance , 1999, KDD '99.

[19]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[20]  Manfred K. Warmuth,et al.  The Weighted Majority Algorithm , 1994, Inf. Comput..

[21]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[22]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..

[23]  William Nick Street,et al.  A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.

[24]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[25]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.