Robustness of classifiers to changing environments

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TypeBook Chapter
Proceedings titleAdvances in Artificial Intelligence : 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
Series titleLecture Notes In Computer Science; Volume 6085
Conference23rd Canadian Conference on Artificial Intelligence (Canadian AI 2010), May 31-June 2, 2010, Ottawa, Ontario, Canada
Pages232243; # of pages: 12
Subjectclassifier evaluation; changing environments; classifier robustness
AbstractIn 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 in uence 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.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number15336798
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Record identifier77119b1a-8896-465f-a80f-d201f5f789d8
Record created2010-06-10
Record modified2016-07-15
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