Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset

Lecture Notes in Control and Information Sciences

Volumn 344, Issue , 2006, Pages 731-740

  Yen, Show Jane ^a   Lee, Yue Shi ^a  

^a MING CHUAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING;

CLASS DISTRIBUTIONS; CLASSIFICATION ACCURACY; CLUSTER-BASED; DISTRIBUTION PROBLEM; IMBALANCED CLASS; IMBALANCED DATASET; REAL-WORLD; SAMPLING TECHNIQUE; TRAINING DATA; UNDER-SAMPLING;

CLASSIFICATION (OF INFORMATION);

EID: 33748947459     PISSN: 01708643     EISSN: None     Source Type: Book Series    
DOI: 10.1007/11816492_89     Document Type: Conference Paper

References (10)

1. Chawla, N. V.: C4.5 and Imbalanced Datasets: Investigating the Effect of Sampling Method, Probabilistic Estimate, and Decision Tree Structure. Proceedings of the ICML'03 Workshop on Class Imbalances, (2003)
2. Chawla, N. V., Bowyer, K.W., Hall, L. O., Kegelmeyer, W. P.: SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research, 16 (2002) 321-357
3. Caragea, D., Cook, D., Honavar, V.: Gaining Insights into Support Vector Machine Pattern Classifiers Using Projection-Based Tour Methods. Proceedings of the KDD Conference, San Francisco, CA (2001) 251-256
4. Chawla, N. V., Lazarevic, A., Hall, L. O., Bowyer, K. W.: Smoteboost: Improving Prediction of the Minority Class in Boosting. Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, Dubrovnik, Croatia (2003) 107-119
5. Clark, P., Niblett, T.: The CN2 Induction Algorithm. Machine Learning, 3 (1989) 261-283
6. Drummond, C., Holte, R. C.: C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling Beats Over-Sampling. Proceedings of the ICML'03 Workshop on Learning from Imbalanced Datasets, (2003)
7. Del-Hoyo, R., Buldain, D., Marco, A.: Supervised Classification with Associative SOM. Lecture Notes in Computer Science, 2686 (2003) 334-341
8. Japkowicz, N.: Concept-learning in the Presence of Between-class and Within-class Imbalances. Proceedings of the Fourteenth Conference of the Canadian Society for Computational Studies of Intelligence, (2001) 67-77
9. Zhang, J., Mani, I.: KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets, (2003).
10. Chyi, Y. M.: Classification Analysis Techniques for Skewed Class Distribution Problems. Master Thesis, Department of Information Management, National Sun Yat-Sen University, (2003)