@inproceedings{9806c3012f8e4887b15923cae807ac4a,
title = "Classifier concept drift detection and the illusion of progress",
abstract = "When a new concept drift detection method is proposed, a common way to show the benefits of the new method, is to use a classifier to perform an evaluation where each time the new algorithm detects change, the current classifier is replaced by a new one. Accuracy in this setting is considered a good measure of the quality of the change detector. In this paper we claim that this is not a good evaluation methodology and we show how a non-change detector can improve the accuracy of the classifier in this setting. We claim that this is due to the existence of a temporal dependence on the data and we propose not to evaluate concept drift detectors using only classifiers.",
keywords = "Classification, Concept drift, Data streams, Evolving, Incremental, Online",
author = "Albert Bifet",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 ; Conference date: 11-06-2017 Through 15-06-2017",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-59060-8\_64",
language = "English",
isbn = "9783319590592",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "715--725",
editor = "Zurada, \{Jacek M.\} and Zadeh, \{Lotfi A.\} and Ryszard Tadeusiewicz and Leszek Rutkowski and Marcin Korytkowski and Rafal Scherer",
booktitle = "Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings",
}