Incremental learning of concept descriptions: A method and experimental results
Machine intelligence 11
Learning flexible concepts from streams of examples: FLORA2
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
Selecting Examples for Partial Memory Learning
Machine Learning
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
Incremental Learning from Noisy Data
Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Batch learning in domains with hidden changes in context
Batch learning in domains with hidden changes in context
Decision trees for mining data streams
Intelligent Data Analysis
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Exploiting temporal contexts in text classification
Proceedings of the 17th ACM conference on Information and knowledge management
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
Temporally-aware algorithms for document classification
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
Fast burst correlation of financial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Resource adaptive periodicity estimation of streaming data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and real concept drift. The algorithm is unsupervised and uses a novel approach to tracking concept drift that involves the use of competing windows to interpret the data. Unlike previous methods which use a single window to determine the drift in the data, our algorithm uses three windows of different sizes to estimate the change in the data. The advantage of this approach is that it allows the system to progressively adapt and predict the change thus enabling it to deal more effectively with different types of drift. We give a detailed description of the algorithm and present the results obtained from its application to two real world problems: background image processing and sound recognition. We also compare its performance with FLORA, an existing concept drift tracking algorithm.