Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Detection of welding flaws from radiographic images with fuzzy clustering methods
Fuzzy Sets and Systems
Extraction of welds from radiographic images using fuzzy classifiers
Information Sciences—Informatics and Computer Science: An International Journal
Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects
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The Journal of Machine Learning Research
Neural Learning from Unbalanced Data
Applied Intelligence
An object detection and recognition system for weld bead extraction from digital radiographs
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The effect of imbalanced data sets on LDA: A theoretical and empirical analysis
Pattern Recognition
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
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Learning from imbalanced data in surveillance of nosocomial infection
Artificial Intelligence in Medicine
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
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IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
MDS: a novel method for class imbalance learning
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IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Expert Systems with Applications: An International Journal
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Information Processing and Management: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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This paper presents research results of our investigation of the imbalanced data problem in the classification of different types of weld flaws, a multi-class classification problem. The one-against-all scheme is adopted to carry out multi-class classification and three algorithms including minimum distance, nearest neighbors, and fuzzy nearest neighbors are employed as the classifiers. The effectiveness of 22 data preprocessing methods for dealing with imbalanced data is evaluated in terms of eight evaluation criteria to determine whether any method would emerge to dominate the others. The test results indicate that: (1) nearest neighbor classifiers outperform the minimum distance classifier; (2) some data preprocessing methods do not improve any criterion and they vary from one classifier to another; (3) the combination of using the AHC_KM data preprocessing method with the 1-NN classifier is the best because they together produce the best performance in six of eight evaluation criteria; and (4) the most difficult weld flaw type to recognize is crack.