Robustness of classifiers to changing environments

Authors:
Houman Abbasian;Chris Drummond;Nathalie Japkowicz;Stan Matwin
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;Institute for Information Technology, National Research Council of Canada, Ottawa, Ontario, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;,School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments The environment may change over time due to some contextual or definitional changes The environment may change with location It would be surprising if the performance of common classifiers did not degrade with these changes The question, we address here, is whether or not some types of classifier are inherently more immune than others to these effects In this study, we simulate the changing of environment by reducing the influence on the class of the most significant attributes Based on our analysis, K-Nearest Neighbor and Artificial Neural Networks are the most robust learners, ensemble algorithms are somewhat robust, whereas Naive Bayes, Logistic Regression and particularly Decision Trees are the most affected.