Handling generalized cost functions in the partitioning optimization problem through sequential binary programming

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 3-9

  Abrahams, Alan S ^a   Fleder, Daniel ^a   Backer, Adrian ^b   MacMillan, Ian C ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING; DECISION TREES; MATRIX ALGEBRA; OPTIMIZATION; PROBLEM SOLVING;

CONSTRAINED CLASSIFICATION; OBJECTIVE FUNCTIONS; SEQUENTIAL BINARY PROGRAMMING;

COST FUNCTIONS;

EID: 34548547051     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.74     Document Type: Conference Paper

References (22)

1. Artin, Michael, Algebra, Prentice Hall, New Jersey, 1991
2. Breiman, Leo, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone, Classification and Regression Trees, Wadsworth International Group, Belmont, California, 1984.
3. Demirsoy, Ipek, Jean-Jacques Fotzeu, and Lisa Worthington, "Caravan Insurance Polices Modeling", Department of Operations and Information Management, University of Pennsylvania, Philadelphia, 2005.
4. Domingos, Pedro. "MetaCost: A General Method for Making Classifiers Cost Sensitive." Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164. 1999.
5. Elkan, Charles, "The Foundations of Cost-Sensitive Learning", Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001.
6. nd International Conference on Distributed Computing Systems", 1063-6927/02 © 2002 IEEE.
7. Granger, C. W. J. 1969. "Prediction with a Generalized Cost of Error Function". Operational Research Quarterly 20(2):199-207.
8. Ingargiola, Giorgio, "Building Classification Models: ID3 and C4.5", Temple University, Available http://www.cis.temple.edu/~ingargio/ cis587/readings/id3-c45.html, 2005
9. Komarek, Paul and Andrew Moore. "Making Logistic Regression a Core Data Mining Tool." Tech. Report CMU-RI-TR-05-27, Robotics Institute, Carnegie Mellon University, May, 2005.
10. Kwedlo, Wojciech, and Marek Kretowski, L. De Raedt, and P. Flach (eds.), "An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning", ECML 2001, LNAI2167, Springer-Verlag Berlin Heidelberg, pp.288-299, 2001.
11. st International Conference on Machine Learning, 2004.
12. Margineantu, Dragos D., "Class Probability Estimation and Cost-Sensitive Classification Decisions", Proceedings, Machine Learning: ECML 2002: 13th European Conference on Machine Learning, Helsinki, Finland, pp.270-281, 2002.
13. Pigatti, Alexandre, Marcus Poggi de Aragão, and Eduardo Uchoa, "Stabilized Branch-and-cut-and-price for the Generalized Assignment Problem", Departmento de Informática, PUC do Rio de Janeiro, 2004
14. Quinlan, Ross J., C4.5 Programs for Machine Learning, Morgan Kaufinann Publishers, San Mateo, California, 1993.
15. Tan, M. 1993. "Cost-Sensitive Learning of Classification Knowledge and its Applications in Robotics". Machine Learning 13:7-33.
16. Ting, K.M, "An Instance Weighting Method to Induce Cost-Sensitive Trees", Paper in International Conference on Data Mining, 2003.
17. Turney, Peter D., "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Algorithm", Journal of Artificial Intelligence Research 2 (1995) p.369-409.
18. Webb, Geoffrey I., "Cost Sensitive Specialization", Proceedings of the Pacific Rim International Conference on Artificial Intelligence, Cairns, Springer-Verlag, pp.23-24, 1996.
19. Van Der Putten, Peter, and M. Van Someren (eds), "CoIL Challenge 2000: The Insurance Company Case", Published by Sentient Machine Research, Amsterdam. Also a Leiden Institute of Advanced Computer Science Technical Report 2000-09. June 22, 2000.
20. Zadrozny, Bianca, and Charles Elkan, "Obtaining Calibrated Probability Estimates from Decision Trees and Naïve Bayes Classifiers", Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01), 2001.
21. Zadrozny, Bianca, "Policy Mining: Learning Decision Policies from Fixed Sets of Data", Ph.D. Thesis, University of California, San Diego, 2003a.
22. Zadrozny, Bianca, John Langford, and Naoki Abe, "Cost-Sensitive Learning by Cost-Proportionate Example Weighting", Proceedings of the Third IEEE International Conference on Data Mining, p.435, 2003b.