Evaluation methods and decision theory for classification of streaming data with temporal dependence

A survey on concept drift adaptation

(2014) João Gama et al.   ACM COMPUTING SURVEYS

Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers

(2013) Konrad Jackowski   PATTERN ANALYSIS AND APPLICATIONS

Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

(2013) Dariusz Brzezinski et al.   IEEE Transactions on Neural Networks and Learning Systems

Incremental Learning of Concept Drift from Streaming Imbalanced Data

(2012) Gregory Ditzler et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Decision rules extraction from data stream in the presence of changing context for diabetes treatment

(2012) Jakub M. Tomczak et al.   KNOWLEDGE AND INFORMATION SYSTEMS

On evaluating stream learning algorithms

(2012) João Gama et al.   MACHINE LEARNING

A robust incremental learning method for non-stationary environments

(2011) David Martínez-Rego et al.   NEUROCOMPUTING

Exponentially weighted moving average charts for detecting concept drift

(2011) Gordon J. Ross et al.   PATTERN RECOGNITION LETTERS

Solving Nonstationary Classification Problems With Coupled Support Vector Machines

(2010) G L Grinblat et al.   IEEE TRANSACTIONS ON NEURAL NETWORKS

: An adaptive classifier for data streams

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