Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning - Special issue on context sensitivity and concept drift
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Incremental learning and concept drift: Editor's introduction: Guest-editorial
Intelligent Data Analysis
Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Statistical Analysis and Data Mining
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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Classification in changing environments (commonly known as concept drift) requires adaptation of the classifier to accommodate the changes. One approach is to keep a moving window on the streaming data and constantly update the classifier on it. Here we consider an abrupt change scenario where one set of probability distributions of the classes is instantly replaced with another. For a fixed 'transition period' around the change, we derive a generic relationship between the size of the moving window and the classification error rate. We derive expressions for the error in the transition period and for the optimal window size for the case of two Gaussian classes where the concept change is a geometrical displacement of the whole class configuration in the space. A simple window resize strategy based on the derived relationship is proposed and compared with fixed-size windows on a real benchmark data set data set (Electricity Market).