Robust regression and outlier detection
Robust regression and outlier detection
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Noise and knowledge acquisition
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Data sets and data quality in software engineering
Proceedings of the 4th international workshop on Predictor models in software engineering
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Data quality in empirical software engineering: a targeted review
Proceedings of the 17th International Conference on Evaluation and Assessment in Software Engineering
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OBJECTIVE - The aim is to report upon an assessment of the impact noise has on the predictive accuracy by comparing noise handling techniques. METHOD - We describe the process of cleaning a large software management dataset comprising initially of more than 10,000 projects. The data quality is mainly assessed through feedback from the data provider and manual inspection of the data. Three methods of noise correction (polishing, noise elimination and robust algorithms) are compared with each other assessing their accuracy. The noise detection was undertaken by using a regression tree model. RESULTS - Three noise correction methods are compared and different results in their accuracy where noted. CONCLUSIONS - The results demonstrated that polishing improves classification accuracy compared to noise elimination and robust algorithms approaches.