International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Instance-Based Learning Algorithms
Machine Learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Communications of the ACM
The impact of poor data quality on the typical enterprise
Communications of the ACM
Enhancing data quality in data warehouse environments
Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Classification of Fault-Prone Software Modules: Prior Probabilities,Costs, and Model Evaluation
Empirical Software Engineering
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
The Necessity of Assuring Quality in Software Measurement Data
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Dealing with predictive-but-unpredictable attributes in noisy data sources
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
Enhancing software quality estimation using ensemble-classifier based noise filtering
Intelligent Data Analysis
Class noise detection using frequent itemsets
Intelligent Data Analysis
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Identifying and eliminating mislabeled training instances
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Relatively little attention has been given in the data mining literature to noise handling procedures that deal specifically with a continuous dependent variable. We present a novel procedure that addresses the problem of detecting and correcting noise when the outcome variable is continuous. Our technique uses a procedure for handling missing data called multiple imputation, a well-known statistical methodology based on sound theoretical principles. We demonstrate the utility of our procedure using a real-world dataset with inherent noise and multiple levels of injected noise in numerous carefully designed controlled experiments. Further, we present a comparison with noise correctors developed using five well-known estimation procedures, providing good coverage of the commonly-used classes of estimation techniques such as linear regression, decision trees and neural networks. The results presented in this work demonstrate conclusively the strong noise detection and correction results of our procedure, which outperforms the five competing noise correctors.