Assessing the impact of class-imbalanced data for classifying relevant/irrelevant medline documents

Advances in Intelligent and Soft Computing

Volumn 93, Issue , 2011, Pages 345-353

  Pavón, Reyes ^a   Laza, Rosalía ^a   Reboiro Jato, Miguel ^a   Fdez Riverola, Florentino ^a  

^a UNIVERSITY OF VIGO   (Spain)

Author keywords

Bayesian networks; document classification; imbalanced data; Medline documents; MeSH terms

Indexed keywords

BAYESIAN NETWORK CLASSIFIERS; CLASS IMBALANCE; COMMON PROBLEMS; CONTROLLED VOCABULARY; DOCUMENT CLASSIFICATION; IMBALANCED DATA; MEDLINE; MEDLINE DOCUMENTS; MESH TERMS;

BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); DATABASE SYSTEMS; INFORMATION RETRIEVAL SYSTEMS;

BAYESIAN NETWORKS;

EID: 80052951721     PISSN: 18675662     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-19914-1_45     Document Type: Conference Paper

References (27)

1. Japkowicz, N., StephenS.: The class imbalance problem: A systematic study. Intell. Data Anal. 6(5), 429 (2002)
2. Van Hulse, J., KhoshgoftaarT.: Knowledge discovery from imbalanced and noisy data. Data & Knowledge Engineering 68, 1513-1542 (2009)
3. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9), 1263-1284 (2009)
4. Dumais, S.T., Platt, J., Heckerman, D., SahamiM.: Inductive learning algorithms and representations for text categorization. In: Proceedings of CIKM 1998, 7th ACM International Conference on Information and Knowledge Management, Bethesda, MD (1998)
5. Lam, W., Low, K.F., Ho, C.Y.: Using a Bayesian network induction approach for text categorization. In: Proceedings of IJCAI 1997, 15th International Joint Conference on Artificial Intelligence, Nagoya, Japan (1997)
6. Yu, T., Jan, T., Simoff, S., DebehamJ.: A hierarchical VQSVM for imbalanced data sets. In: Proceedings of the International Joint Conference on Neural Networks, Orlando, Florida (2007)
7. ZhuX.: Lazy Bagging for Classifying Imbalanced Data. In: Proceedings of the 7th IEEE International Conference on Data Mining, Omaha NE, USA (2007)
8. FormanG.: An extensive empirical study of feature selection metrics for text classification. Journal of Machine Learning Research 3, 1289-1305 (2003)
9. Chen, X., WasikowskiM.: FAST: A ROC-based feature selection metric for small samples and imbalanced data classification problems. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas (2008)
10. Glez-Peña, D., López, S., Pavón, R., Laza, R., Iglesias, E.L., BorrajoL.: Classification of Medline documents using MeSH terms. In: Proceedings of the 4th International Workshop on Practical Applications of Computational Biology & Bioinformatics, Salamanca, Spain (2009)
11. Ng, W., DashM.: An evaluation of progressive sampling for imbalanced data sets. In: Proceedings of the 6th IEEE International Conference on Data Mining -Workshops, Hong Kong, China (2006)
12. Yen, S.J., Lee, Y.S., Lin, C.H., Ying, J.C.: Investigating the effect of sampling methods for imbalanced data distributions. In: Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan (2006)
13. Japkowicz, N., StephenS.: The class imbalance problem: A systematic study. Intell. Data Anal. 6, 429-449 (2002)
14. Kubat, M., MatwinS.: Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, Nashville, Tennessee, USA (1997)
15. Drummond, C., HolteR.: C4.5, class imbalance, and cost sensitivity: why undersampling beats oversampling. In: Proceedings of the ICML2003 -Workshop on Learning from Imbalanced Data Sets, Washington, DC USA (2003)
16. ElkanC.: The foundations of cost-sensitive learning. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, Seattle, Washington, USA (2001)
17. Domingos P.: Metacost: A general method for making classifiers costsensitive. In: Proceedings of the Fifth ACM SIGKDD International Conference Knowledge Discovery and Data Mining, San Diego, CA (1999)
18. Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. Proceedings of the IEEE Transactions on Knowledge and Data Engineering (2006)
19. Liu, X.Y., Zhou, Z.H.: The influence of class imbalance on cost-sensitive learning: An empirical study. In: Proceedings of the 6th International Conference on Data Mining, Hong Kong, China (2006)
20. Japkowicz, N., Myers, C., GluckM.: A novelty detection approach to classification. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada (1995)
21. Kubat, M., Holte, R., MatwinS.: Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30, 195-215 (1998)
22. Molinara, M., Ricamato, M.T., TortorellaF.: Facing imbalanced classes through aggregation of classifiers. In: Proceedings of the 14th International Conference on Image Analysis and Processing, Modena, Italy (2007)
23. Ertekin, S., Huang, J., Giles, C.L.: Active learning for class imbalance problem. In: Proceedings of the 21st annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Amsterdam, Netherlands (2007)
24. Ertekin, S., Huang, J., Bottou, L., Giles, C.L.: Learning on the border: Active learning in imbalanced data classification. In: Proceedings of the ACM Sixteenth Conference on Information and Knowledge Management, Lisboa, Portugal (2007)
25. Cooper, G.F., HerskovitsE.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9(4), 309-347 (1992)
26. Zhang, J., ManiI.: kNN approach to unbalanced data distributions: A case study involving information extraction. In: Proceedings of the ICML 2003 workshop on learning from imbalanced datasets, Washigton DC, USA (2003)
27. He, H., Bai, Y., Garcia, E.A., LiS.: ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the Int. Joint Conference on Neural Networks, IJCNN 2008, Hong Kong, China (2008)