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Concept drift refers to a non stationary learning problem over time.
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G. Widmer and M. Kubat · 1993
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Detecting concept drift in financial time series prediction using symbolic machine learning
M. Harries and K. Horn · 1995
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G. Widmer and M. Kubat · 1996
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Lazy learning
D. Aha · 1997
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Case-based reasoning: an overview
R. de Mantaras and E. Enric · 1997
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Adaptive resonance theory (art)
G. Carpenter and S. Grossberg · 1998
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Special issue on context sensitivity and concept drift
T. Dietterich, G. Widmer, and M. Kubat · 1998
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Extracting hidden context
M. Harries, C. Sammut, and K. Horn · 1998
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Adaptive information filtering: Learning drifting concepts
R. Klinkenberg and I. Renz · 1998
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A hybrid user model for news story classification
D. Billsus and M. Pazzani · 1999
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A short introduction to boosting
Y. Freund and R. Schapire · 1999
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The impact of changing populations on classifier performance
M. Kelly, D. Hand, and N. Adams · 1999
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Temporal sequence learning and data reduction for anomaly detection
T. Lane and C. Brodley · 1999
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Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
T. Sung, N. Chang, and G. Lee · 1999
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Handling concept drifts in incremental learning with support vector machines
N. Syed, H. Liu, and K. Sung · 1999
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Constructing domain ontologies based on concept drift analysis
T. Yamaguchi · 1999
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Pattern Classification (2nd Edition)
R. Duda, P. Hart, and D. Stork · 2000
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A note on the utility of incremental learning
Ch. Giraud-Carrier · 2000
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Detecting concept drift with support vector machines
R. Klinkenberg and T. Joachims · 2000
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Gradual forgetting for adaptation to concept drift
I. Koychev · 2000
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Smart adaptive systems: State of the art and future directions of research
D.Anguita · 2001
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Mining time-changing data streams
G. Hulten, L. Spencer, and P. Domingos · 2001
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A streaming ensemble algorithm (sea) for large-scale classification
N. Street and Y. Kim · 2001
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Classification of customer call data in the presence of concept drift and noise
M. Black and R. Hickey · 2002
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Statistical fraud detection: A review
R. Bolton and D. Hand · 2002
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Incremental machine learning to reduce biochemistry lab costs in the search for drug discovery
G. Forman · 2002
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Bursty and hierarchical structure in streams
J. Kleinberg · 2002
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Online classification of nonstationary data streams
M. Last · 2002
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A unifying view on instance selection
T. Reinartz · 2002
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A survey of temporal knowledge discovery paradigms and methods
J. Roddick and M. Spiliopoulou · 2002
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Aha! the adaptive hypermedia architecture
P. De Bra, A. Aerts, B. Berden, B. de Lange, B. Rousseau, T. Santic, D. Smits, and N. Stash · 2003
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”in vivo” spam filtering: a challenge problem for kdd
T. Fawcett · 2003
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Drifting concepts as hidden factors in clinical studies
M. Kukar · 2003
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Using selective memory to track concept effectively
M. Lazarescu and S. Venkatesh · 2003
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Learning concept drift with a committee of decision trees
K. Stanley · 2003
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Mining concept-drifting data streams using ensemble classifiers
H. Wang, W. Fan, P. Yu, and J. Han · 2003
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Concept drift learning and its application to adaptive information filtering
D. Widyantoro · 2003
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Detecting and adapting to concept drift in bioinformatics
M. Black and R. Hickey · 2004
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Fast and light boosting for adaptive mining of data streams
Fang Chu and Carlo Zaniolo · 2004
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Early detection of insider trading in option markets
S. Donoho · 2004
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Systematic data selection to mine concept-drifting data streams
W. Fan · 2004
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Learning with drift detection
J. Gama, P. Medas, G. Castillo, and P. Rodrigues · 2004
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Learning drifting concepts: Example selection vs. example weighting
R. Klinkenberg · 2004
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Special issue on incremental learning systems capable of. dealing with concept drift
M. Kubat, J. Gama, and P. Utgoff · 2004
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Using multiple windows to track concept drift
M. Lazarescu, S. Venkatesh, and H. Bui · 2004
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Change detection techniques
D. Lu, P. Mausel, E. Brondizio, and E. Moran · 2004
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Designing Evolutionary Algorithms for Dynamic Environments
R. Morrison · 2004
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Literature review of spatio-temporal database models
N. Pelekis, B. Theodoulidis, I. Kopanakis, and Y. Theodoridis · 2004
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Player-centred game design: Player modelling and adaptive digital games
D. Charles, A. Kerr, M. McNeill, M. McAlister, M. Black, J. Kücklich, A. Moore, and K. Stringer · 2005
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A methodology for dynamic data mining based on fuzzy clustering
F. Crespo and R. Weber · 2005
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A case-based technique for tracking concept drift in spam filtering
S. Delany, P. Cunningham, A. Tsymbal, and L. Coyle · 2005
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Time weight collaborative filtering
Y. Ding and X. Li · 2005
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Mining data streams: a review
M. Gaber, A. Zaslavsky, and S. Krishnaswamy · 2005
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Meta-learning, model selection and example selection in machine learning domains with concept drift
R. Klinkenberg · 2005
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An adaptive nearest neighbor classification algorithm for data streams
Y. Law and C. Zaniolo · 2005
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Ace: Adaptive classifiers-ensemble system for concept-drifting environments
K. Nishida, K. Yamauchi, and T. Omori · 2005
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To transfer or not to transfer
M. Rosenstein, Z. Marx, L. Kaelbling, and T. Dietterich · 2005
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A notion of task relatedness yielding provable multiple-task learning guarantees
S. Ben-David and R. Borbely · 2008
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Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman · 2008
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Predicting future decision trees from evolving data
M. Bottcher, M. Spott, and R. Kruse · 2008
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Online probability density estimation of nonstationary random signal using dynamic bayesian networks
H. Cho, M. Fadali, and K. Lee · 2008
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Learning from multiple sources
K. Crammer, M. Kearns, and J. Wortman · 2008
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Classifying data streams with skewed class distributions and concept drifts
J. Gao, B. Ding, W. Fan, J. Han, and P. Yu · 2008
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Association mining in time-varying domains
A. Rozsypal and M. Kubat · 2005
Cited alongside, same era.
Relevant data expansion for learning concept drift from sparsely labeled data
D. Widyantoro and J. Yen · 2005
Cited alongside, same era.
Tracking concept drifting with an online-optimized incremental learning framework
J. Wu, D. Ding, X. Hua, and B. Zhang · 2005
Cited alongside, same era.
Early drift detection method
M. Baena-Garcia, J. del Campo-Avila, R. Fidalgo, A. Bifet, R. Gavalda, and R. Morales-Bueno · 2006
Cited alongside, same era.
Dynamic topic models
D. Blei and J. Lafferty · 2006
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Adaptive Learning Algorithms for Bayesian Network Classifiers
G. Castillo · 2006
Cited alongside, same era.
Towards intelligent and adaptive digital library services
M. Hasan and E. Nantajeewarawat · 2008
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Data mining for multiple antibiotic resistance
C. Jermaine · 2008
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Learning concept drift in nonstationary environments using an ensemble of classifiers based approach
M. Karnick, M. Ahiskali, M. Muhlbaier, and R. Polikar · 2008
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An ensemble of classifiers for coping with recurring contexts in data streams
I. Katakis, G. Tsoumakas, and I. P. Vlahavas · 2008
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knn cf: a temporal social network
N. Lathia, S. Hailes, and L. Capra · 2008
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Local likelihood modeling of temporal text streams
G. Lebanon and Y. Zhao · 2008
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Domain adaptation with multiple sources
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2008
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Research challenges in ubiquitous knowledge discovery
M. May, B. Berendt, A. Cornuejols, J. Gama, F. Giannotti, A. Hotho, D. Malerba, E. Menasalvas, K. Morik, R. Pedersen, L. Saitta, Y. Saygin, A. Schuster, and K. Vanhoof · 2008
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Travel time prediction for the planning of mass transit companies: a machine learning approach
J. Moreira · 2008
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Understanding temporal aspects in document classification
F. Mourao, L. Rocha, R. Araujo, T. Couto, M. Goncalves, and W. Meira · 2008
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Learning and Detecting Concept Drift
K. Nishida · 2008
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A cellular automata approach to detecting concept drift and dealing with noise
M. Pourkashani and M. Kangavari · 2008
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Combining online classification approaches for changing environments
J. Rodriguez and L. Kuncheva · 2008
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Dynamicweb: Adapting to concept drift and object drift in cobweb
J. Scanlan, J. Hartnett, and R. Williams · 2008
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A bayesian mixture model with linear regression mixing proportions
X. Song, C. Jermaine, S. Ranka, and J. Gums · 2008
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Novelty Detection in Data Streams
E. Spinosa · 2008
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Modeling concept drift from the perspective of classifiers
B. Su, Y. Shen, and W. Xu · 2008
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Dynamic integration of classifiers for handling concept drift
A. Tsymbal, M. Pechenizkiy, P. Cunningham, and S. Puuronen · 2008
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Continuous time dynamic topic models
C. Wang, D. Blei, and D. Heckerman · 2008
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Population-based incremental learning with associative memory for dynamic environments
S. Yang and X. Yao · 2008
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Conceptual equivalence for contrast mining in classification learning
Y. Yang, X. Wu, and X. Zhu · 2008
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Categorizing and mining concept drifting data streams
P. Zhang, X. Zhu, and Y. Shi · 2008
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Prediction and change detection in sequential data for interactive applications
J. Zhou, L. Cheng, and W. Bischof · 2008
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Handling outliers and concept drift in online mass flow prediction in cfb boilers
J. Bakker, M. Pechenizkiy, I. Zliobaite, A. Ivannikov, and T. Karkkainen · 2009
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Adaptive Learning and Mining for Data Streams and Frequent Patterns
A. Bifet · 2009
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New ensemble methods for evolving data streams
A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavalda · 2009
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Detecting and predicting changes
S. Brown and M. Steyvers · 2009
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Anomaly detection - a survey
V. Chandola, A. Banerjee, and V. Kumar · 2009
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Adaptive concept drift detection
A. Dries and U. Ruckert · 2009
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Tracking concept drift of software projects using defect prediction quality
J. Ekanayake, J. Tappolet, H. Gall, and A. Bernstein · 2009
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Designing an expert system for fraud detection in private telecommunications networks
C. Hilas · 2009
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Data pre-processing of bankruptcy prediction models using data mining techniques
R. Horta, B. de Lima, and C. Borges · 2009
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Classification without discrimination
F. Kamiran and T. Calders · 2009
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Tracking recurring contexts using ensemble classifiers: an application to email filtering
I. Katakis, G. Tsoumakas, and I. Vlahavas · 2009
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Collaborative filtering with temporal dynamics
Y. Koren · 2009
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Flood pattern detection using sliding window technique
K. Ku-Mahamud, N. Zakaria, N. Katuk, and M. Shbier · 2009
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A multi-partition multi-chunk ensemble technique to classify concept-drifting data streams
M. Masud, J. Gao, L. Khan, J. Han, and B. Thuraisingham · 2009
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Motivated learning from interesting events: Adaptive, multitask learning agents for complex environments
K. Merrick and M. Maher · 2009
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The impact of diversity on on-line ensemble learning in the presence of concept drift
L. Minku, A. White, and X. Yao · 2009
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Challenges and research directions for adaptive biometric recognition systems
N. Poh, R. Wong, J. Kittler, and F. Roli · 2009
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Learning terrain segmentation with classifier ensembles for autonomous robot navigation in unstructured environments
M. Procopio, J. Mulligan, and G. Grudic · 2009
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Dataset Shift in Machine Learning
J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. Lawrence · 2009
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Keeping the resident in the loop: Adapting the smart home to the user
P. Rashidi and D. Cook · 2009
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Learning behavior in abstract memory schemes for dynamic optimization problems
H. Richter and S. Yang · 2009
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Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
P. Rohlfshagen, P. Lehre, and X. Yao · 2009
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Active learning literature survey
B. Settles · 2009
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Mining decision rules on data streams in the presence of concept drifts
C. Tsai, C. Lee, and W. Yang · 2009
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Clan: Clustering for credit risk assessment
I. Zliobaite and T. Krilavicius · 2009
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Determining the training window for small sample size classification with concept drift
I. Zliobaite and L. Kuncheva · 2009
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