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2010
Springer

Robustness of Classifiers to Changing Environments

8 years 6 months ago
Robustness of Classifiers to Changing Environments
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. Key words: classifier evaluation, changing environments, classifier robustness
Houman Abbasian, Chris Drummond, Nathalie Japkowic
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where AI
Authors Houman Abbasian, Chris Drummond, Nathalie Japkowicz, Stan Matwin
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