C4.5: programs for machine learning
C4.5: programs for machine learning
Software metrics (2nd ed.): a rigorous and practical approach
Software metrics (2nd ed.): a rigorous and practical approach
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Experiments with Noise Filtering in a Medical Domain
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A Comparison of Noise Handling Techniques
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
A geometric theory of outliers and perturbation
A geometric theory of outliers and perturbation
Analyzing Software Measurement Data with Clustering Techniques
IEEE Intelligent Systems
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
Empirical Software Engineering
The Necessity of Assuring Quality in Software Measurement Data
METRICS '04 Proceedings of the Software Metrics, 10th International Symposium
Cost-Guided Class Noise Handling for Effective Cost-Sensitive Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
The pairwise attribute noise detection algorithm
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Enhancing software quality estimation using ensemble-classifier based noise filtering
Intelligent Data Analysis
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
A comprehensive empirical evaluation of missing value imputation in noisy software measurement data
Journal of Systems and Software
A pattern-based outlier detection method identifying abnormal attributes in software project data
Information and Software Technology
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An important issue in the analysis of data is that of data quality. Algorithms for the detection of instances with class noise and, to a lesser degree, attribute noise have been presented in the literature. We propose a novel technique to detect noisy instances relative to an Attribute of Interest. The attribute of interest can be any feature from the dataset as defined by the domain-specific practitioner. The proposed technique determines those instances that contain noise relative to the chosen attribute of interest. This approach can be iterated for any number of user-specified attributes. Our methodology is demonstrated with empirical case studies using real-world datasets and is verified by an expert-based validation of the results. Additional studies show the effectiveness of our technique in detecting noise injected into instances. For detecting noise relative to the class or dependent variable, our technique is compared to the well-known classification and ensemble filters and outperforms both techniques on a real-world dataset with known class noise. Based on the results of a wide variety of case studies presented in this work, we conclude that our methodology for ranking noisy instances relative to an attribute of interest is an effective and useful noise handling procedure.