Learning in the presence of concept drift and hidden contexts
Machine Learning
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Online Ensemble Learning: An Empirical Study
Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Combining Online Classification Approaches for Changing Environments
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
IEEE Transactions on Knowledge and Data Engineering
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Margin calibration in SVM class-imbalanced learning
Neurocomputing
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
λ-Perceptron: An adaptive classifier for data streams
Pattern Recognition
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
Classification Using Streaming Random Forests
IEEE Transactions on Knowledge and Data Engineering
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IEEE Transactions on Knowledge and Data Engineering
Incremental learning with multi-level adaptation
Neurocomputing
Online non-stationary boosting
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
DDD: A New Ensemble Approach for Dealing with Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Incremental Learning of Concept Drift in Nonstationary Environments
IEEE Transactions on Neural Networks
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Concept drift (non-stationarity) and class imbalance are two important challenges for supervised classifiers. ''Concept drift'' (or non-stationarity) refers to changes in the underlying function being learnt, and class imbalance is a vast difference between the numbers of instances in different classes of data. Class imbalance is an obstacle for the efficiency of most classifiers. Research on classification of non-stationary and imbalanced data streams, mainly focuses on batch solutions, whereas online methods are more appropriate. Here, we propose an online ensemble of neural network (NN) classifiers. Ensemble models are the most frequent methods used for classifying non-stationary and imbalanced data streams. The main contribution is a two-layer approach for handling class imbalance and non-stationarity. In the first layer, cost-sensitive learning is embedded into the training phase of the NNs, and in the second layer a new method for weighting classifiers of the ensemble is proposed. The proposed method is evaluated on 3 synthetic and 8 real-world datasets. The results show statistically significant improvement compared to online ensemble methods with similar features.