Elements of information theory
Elements of information theory
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
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Machine Learning - Special issue on context sensitivity and concept drift
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The application of AdaBoost for distributed, scalable and on-line learning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Experience with personalization of Yahoo!
Communications of the ACM
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Information-theoretic fuzzy approach to data reliability and data mining
Fuzzy Sets and Systems
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Discovering Robust Knowledge from Databases that Change
Data Mining and Knowledge Discovery
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The data mining approach to automated software testing
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
Incremental Learning of Linear Model Trees
Machine Learning
Immune-inspired incremental feature selection technology to data streams
Applied Soft Computing
Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Info-fuzzy algorithms for mining dynamic data streams
Applied Soft Computing
Handling Missing Data from Heteroskedastic and Nonstationary Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A new framework for an adaptive classifier model
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Classification of EEG for Affect Recognition: An Adaptive Approach
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
FAW'07 Proceedings of the 1st annual international conference on Frontiers in algorithmics
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Affective modeling from multichannel physiology: analysis of day differences
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Classifying noisy data streams
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Discovery and diagnosis of behavioral transitions in patient event streams
ACM Transactions on Management Information Systems (TMIS)
Continuous trend-based classification of streaming time series
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
Event-based classification of social media streams
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Online activity recognition using evolving classifiers
Expert Systems with Applications: An International Journal
Learning deep belief networks from non-stationary streams
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Information Sciences: an International Journal
Concept drift detection via competence models
Artificial Intelligence
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Most classification methods are based on the assumption that the data conforms to a stationary distribution. However, the real-world data is usually collected over certain periods of time, ranging from seconds to years, and ignoring possible changes in the underlying concept, also known as concept drift, may degrade the predictive performance of a classification model. Moreover, the computation time, the amount of required memory, and the model complexity may grow indefinitely with the continuous arrival of new training instances. This paper describes and evaluates OLIN, an online classification system, which dynamically adjusts the size of the training window and the number of new examples between model re-constructions to the current rate of concept drift. By using a fixed amount of computer resources, OLIN produces models, which have nearly the same accuracy as the ones that would be produced by periodically re-constructing the model from all accumulated instances. We evaluate the system performance on sample segments from two real-world streams of non-stationary data.