Boosting trees for cost-Sensitive classifications

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1398, Issue , 1998, Pages 190-195

  Ting, Kai Ming ^a   Zheng, Zijian ^b  

^a UNIVERSITY OF WAIKATO   (New Zealand)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COSTS; FORESTRY;

BOOSTING TREES; COST CHANGES; COST SENSITIVE CLASSIFICATIONS; COST-SENSITIVE; MISCLASSIFICATION COSTS; MULTIPLE TREES; ROBUST PREDICTIONS;

LEARNING SYSTEMS;

EID: 84957062500     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (7)

1. Merz, C.J. & P.M. Murphy (1997), UCI Repository of machine learning databaseshttp://www.ics.uci.edu/mlearn/MLRepository.htmll. Irvine, CA: University of California, Department of Information and Computer Science.
2. Michie, D., D.J. Spiegelhalter, & C.C. Taylor (1994), Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited.
3. Pazzani, M., C. Merz, P. Murphy, K. Ali, T. Hume, & C. Brunk (1994), Reducing misclassification costs, in Proceedings of the Eleventh International Conference on Machine Learning, pp. 217-225, Morgan Kaufmann.
4. Quinlan, J.R. (1993), C4.5: Program for machine learning, Morgan Kaufmann.
5. Quinlan, J.R. (1996), Bagging, boosting, and C4.5, in Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725-730, AAAI Press.
6. Schapire, Y. Freund, P. Bartlett, & W.S. Lee (1997), Boosting the margin: A new explanation for the effectiveness of voting methods, in Proceedings of the Fourteenth International Conference on Machine Learning, pp. 322-330.
7. Ting, K.M. & Z. Zheng (1998), Boosting Trees for Cost-Sensitive Classifications, Working Paper 1/98, Dept. of Computer Science, University of Waikato.