C4.5: programs for machine learning
C4.5: programs for machine learning
Incremental Learning from Noisy Data
Machine Learning
Tree Induction for Probability-Based Ranking
Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A framework for monitoring classifiers’ performance: when and why failure occurs?
Knowledge and Information Systems
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
New options for hoeffding trees
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
The Journal of Machine Learning Research
Adaptive methods for classification in arbitrarily imbalanced and drifting data streams
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
| Hi-index | 0.00 |
Learning in non-stationary environments is an increasingly important problem in a wide variety of real-world applications. In non-stationary environments data arrives incrementally, however the underlying generating function may change over time. In addition to the environments being non-stationary, they also often exhibit class imbalance. That is one class (the majority class) vastly outnumbers the other class (the minority class). This combination of class imbalance with non-stationary environments poses significant and interesting practical problems for classification. To overcome these issues, we introduce a novel instance selection mechanism, as well as provide a modification to the Heuristic Updatable Weighted Random Subspaces (HUWRS) method for the class imbalance problem. We then compare our modifications of HUWRS (called HUWRS.IP) to other state of the art algorithms, concluding that HUWRS. IP often achieves vastly superior performance.