Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mahalanobis-Taguchi System
Image Representations and Feature Selection for Multimedia Database Search
IEEE Transactions on Knowledge and Data Engineering
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Blocking Reduction Strategies in Hierarchical Text Classification
IEEE Transactions on Knowledge and Data Engineering
KBA: Kernel Boundary Alignment Considering Imbalanced Data Distribution
IEEE Transactions on Knowledge and Data Engineering
The class imbalance problem: A systematic study
Intelligent Data Analysis
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Learning classifiers from imbalanced data based on biased minimax probability machine
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
International Journal of Approximate Reasoning
MDS: a novel method for class imbalance learning
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information Sciences: an International Journal
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets
Pattern Recognition Letters
Distributed road surface condition monitoring using mobile phones
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Applying the Mahalanobis-Taguchi strategy for software defect diagnosis
Automated Software Engineering
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Empirical study of bagging predictors on medical data
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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In classification problems, class imbalance problem will cause bias on the training of classifiers, and will result in the lower sensitivity of detecting the minority class examples. Mahalabobis-Taguchi System (MTS) is a diagnosis and forecasting technique for multivariate data. MTS establishes a classifier by constructing a continuous measurement scale rather than directly learning from the training set. Therefore, it is expected that the construction of an MTS model will not be influenced by data distribution, and this property is helpful to overcome the class imbalance problem. To verify the robustness of MTS for imbalanced data, this study compares MTS with several popular classification techniques. The results indicate that MTS is the most robust technique to deal with the classification problem on imbalanced data. In addition, this study develops a "probabilistic thresholding method" to determine the classification threshold for MTS, and it obtains a good performance. Finally, MTS is employed to analyze the RF inspection process of mobile phone manufacture. The data collected from the RF inspection process is typically an imbalanced type. Implementation results show that the inspection attributes are significantly reduced and that the RF inspection process can also maintain high inspection accuracy.