The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Robust Classification for Imprecise Environments
Machine Learning
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Asymptotic Bayesian generalization error when training and test distributions are different
Proceedings of the 24th international conference on Machine learning
Quantification and semi-supervised classification methods for handling changes in class distribution
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A unifying view on dataset shift in classification
Pattern Recognition
Expert Systems with Applications: An International Journal
Robustness of classifiers to changing environments
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Handling concept drift via ensemble and class distribution estimation technique
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Statistical Analysis and Data Mining
Class distribution estimation based on the Hellinger distance
Information Sciences: an International Journal
Information Sciences: an International Journal
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The purpose of this paper is to test the hypothesis that simple classifiers are more robust to changing environments than complex ones. We propose a strategy for generating artificial, but realistic domains, which allows us to control the changing environment and test a variety of situations. Our results suggest that evaluating classifiers on such tasks is not straightforward since the changed environment can yield a simpler or more complex domain. We propose a metric capable of taking this issue into consideration and evaluate our classifiers using it. We conclude that in mild cases of population drifts simple classifiers deteriorate more than complex ones and that in more severe cases as well as in class definition changes, all classifiers deteriorate to about the same extent. This means that in all cases, complex classifiers remain more accurate than simpler ones, thus challenging the hypothesis that simple classifiers are more robust to changing environments than complex ones.