Incremental Learning of Concept Drift in Nonstationary Environments

Classification Using Streaming Random Forests

(2010) Hanady Abdulsalam et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Learn++.MF: A random subspace approach for the missing feature problem

(2010) Robi Polikar et al.   PATTERN RECOGNITION

: An adaptive classifier for data streams

(2010) N.G. Pavlidis et al.   PATTERN RECOGNITION

The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift

(2009) L.L. Minku et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Sequential non-stationary dynamic classification with sparse feedback

(2009) D.R. Lowne et al.   PATTERN RECOGNITION

Info-fuzzy algorithms for mining dynamic data streams

(2008) Lior Cohen et al.   APPLIED SOFT COMPUTING

Classifying Data Streams with Skewed Class Distributions and Concept Drifts

(2008) Jing Gao et al.   IEEE INTERNET COMPUTING

Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier

(2008) Cesare Alippi et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

IMORL: Incremental Multiple-Object Recognition and Localization

(2008) Haibo He et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes

(2008) C. Alippi et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Learn$^{++}$.NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes

(2008) M.D. Muhlbaier et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

Mining decision rules on data streams in the presence of concept drifts

(2007) Cheng-Jung Tsai et al.   EXPERT SYSTEMS WITH APPLICATIONS

Dynamic integration of classifiers for handling concept drift

(2007) Alexey Tsymbal et al.   Information Fusion

Real-time data mining of non-stationary data streams from sensor networks

(2005) Lior Cohen et al.   Information Fusion