Learning rules from highly unbalanced data sets

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 571-574

  Zhang, Jianping ^a   Bloedorn, Eric ^b   Rosen, Lowell ^b   Venese, Daniel ^b  

^a AOL Inc   (United States)

^b MITRE CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; OPERATIONAL ENVIRONMENTS; RULE LEARNING FOR SKEWED DATA (RLSD); TRAINING SETS;

DATA REDUCTION; FUNCTIONS; LEARNING ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; SET THEORY;

DATA MINING;

EID: 19544387403     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Japkowicz N. and Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent Data Analysis. 6(5).
2. Ling, C.X., and Li, C. (1998). Data mining for direct marketing: Problems and solutions. Proceedings of The Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 73-79, New York, NY, AAAI Press.
3. Kubat, M. & Matwin, S. (1997). Addressing the curse of imbalanced data sets: One-sided sampleing. Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179-186. Morgan Kaufmann.
4. Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155-164, San Diego, CA.
5. Kubat, M., Holte, R., and Matwin, S. (1998). Machine learning for the detection of oil spills in satellite radar images. Machine Learning 30, pp. 195-215.
6. Kerber, R. (1992). ChiMerge: Discretization of Numeric Attributes. Proceedings of the Tenth National Conference on Artificial Interlligence, pp. 123-128, San Jose, CA.