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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Arjun Pakrashi Brian Mac Namee |
| Abstract | Boosting has been shown to be a very effective approach to training ensemble classification models. Although they perform very well, boosting algorithms are sensitive to class-label noise (where training data instances are mislabelled). As the level of class-label noise in the training dataset increases, the generalisation performance of ensembles trained using boosting decreases. This paper introduces KalmanTune, a tuning process that can be applied to ensemble models after they have been trained using a boosting algorithm that reduces the impact of class-label noise. KalmanTune frames the tuning of a trained ensemble model as a static state estimation problem that can be addressed using a Kalman filter. This approach exploits the sensor fusion capability of the Kalman filter to reduce the impact of class-label noise on the trained ensemble. This paper describes KalmanTune and an evaluation experiment performed using 34 multi-class datasets with 5 levels of synthetically induced class-label noise that demonstrates that applying KalmanTune after training can improve the performance of ensemble models trained using boosting, especially when training data contains noisy class-labels. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3013908 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Multi-class Classification Ensemble Kalman Filter |
| Content Type | Text |
| Resource Type | Article |
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