Selected Papers and Presentations Presented at MODSIM World 2010 Conference & Expo
MODSIM World 2010 Conference & Expo, October 13-15, 2010, Hampton, Virginia
This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classis and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random under-sampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving test dataset show that accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes
"Distribution/Availability Statement: Unclassified - Unlimited"
Original Publication Citation
Feng, L., McKenzie, F., Li, J., Zhang, G., Xu, R., Richey, C., & Schnell, T. (2011). Imbalanced learning for functional state assessment. In T.E. Pinelli (Ed.), Selected papers and presentations presented at MODSIM World 2010 Conference & Expo (pp. 675-700). NASA Center for Aerospace Information.
Li, Feng; McKenzie, Frederick; Li, Jiang; Zhang, Guanfan; Xu, Roger; Richey, Carl; Schnell, Tom; and Pinelli, Thomas E. (Ed.), "Imbalanced Learning for Functional State Assessment" (2011). Electrical & Computer Engineering Faculty Publications. 365.