Algorithm portfolios based on cost-sensitive hierarchical clustering

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 608-614

  Malitsky, Yuri ^a   Sabharwal, Ashish ^b   Samulowitz, Horst ^b   Sellmann, Meinolf ^b  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM PORTFOLIOS; ALGORITHMIC APPROACH; COMBINATORIAL PROBLEM; COST-SENSITIVE; HIER-ARCHICAL CLUSTERING; MAX-SAT; PROBLEM INSTANCES; SOLUTION APPROACH;

ARTIFICIAL INTELLIGENCE;

ALGORITHMS;

EID: 84896063318     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Roberto Amadini, Maurizio Gabbrielli, and Jacopo Mauro. An empirical evaluation of portfolios approaches for solving csps. In CPAIOR-2013, 2013.
2. Charles Elkan. The foundations of costsensitive learning. In IJCAI-01, pages 973-978, 2001.
3. Peter Geibel and Fritz Wysotzki. Learning perceptrons and piecewise linear classifiers sensitive to example dependent costs. Appl. Intell., 21(1):45-56, 2004.
4. C.P. Gomes and B. Selman. Algorithm portfolios. Artificial Intelligence Journal, 126(1-2):43-62, 2001.
5. Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. J. Mach. Learn. Res., 3:1157-1182, 2003.
6. S. Kadioglu, Y. Malitsky, M. Sellmann, and K. Tierney. Isac-instance-specific algorithm configuration. Proc. of the 19th European Conference on Artificial Intelligence (ECAI), pages 751-756, 2010.
7. S. Kadioglu, Y. Malitsky, A. Sabharwal, H. Samulowitz, and M. Sellmann. Algorithm selection and scheduling. CP, 2011.
8. Ralf Klinkenberg and Stefan Rüping. Concept drift and the importance of example. In Text Mining, pages 55-78, 2003.
9. Andrzej Lenarcik and Zdzislaw Piasta. Rough classifiers sensitive to costs varying from object to object. In Rough Sets and Current Trends in Computing, pages 222-230, 1998.
10. Yuri Malitsky. Algorithm portfolio benchmark set, 2013. http://4c.ucc.ie/-ymalitsky/algorithm-portfolio-benchmark-set.html.
11. E. O'Mahony, E. Hebrard, A. Holland, C. Nugent, and B. O'Sullivan. Using case-based reasoning in an algorithm portfolio for constraint solving. Irish Conference on Artificial Intelligence and Cognitive Science, 2008.
12. B. Silverthorn and R. Miikkulainen. Latent class models for algorithm portfolio methods. AAAI, 2010.
13. D. Stern, H. Samulowitz, R. Herbrich, T. Graepel, L. Pulina, and A. Tacchella. Collaborative expert portfolio management. AAAI, 2010.
14. M. Streeter and S.F. Smith. Using decision procedures efficiently for optimization. ICAPS, pages 312-319, 2007.
15. K. M. Ting. An instance-weighting method to induce cost-sensitive trees. IEEE Trans. on Knowl. and Data Eng., 14(3):659-665, 2002.
16. Fritz Wysotzki and Peter Geibel. A new information measure based on exampledependent misclassification costs and its application in decision tree learning. Adv. Artificial Intellegence, 2009.
17. L. Xu, F. Hutter, H.H. Hoos, and K. Leyton-Brown. Satzilla: Portfolio-based algorithm selection for sat. JAIR, 32(1):565-606, 2008.
18. L. Xu, F. Hutter, J. Shen, H.H. Hoos, and K. Leyton-Brown. Satzilla2012: Improved algorithm selection based on cost-sensitive classification models. solver description, 2012. SAT Challenge 2012.
19. Lin Xu, Frank Hutter, Holger Hoos, and Kevin Leyton-Brown. Evaluating component solver contributions to portfolio-based algorithm selectors. In SAT-2012, pages 228-241, 2012.
20. Bianca Zadrozny, John Langford, and Naoki Abe. Cost-sensitive learning by costproportionate example weighting. In 3rd Intl. Conf. on Data Mining, pages 435-442, 2003