Cost-conscious classifier ensembles

Pattern Recognition Letters

Volumn 26, Issue 14, 2005, Pages 2206-2214

  Demir, Cigdem ^a,b   Alpaydin, Ethem ^a  

^a BOGAZICI UNIVERSITY   (Turkey)

^b RENSSELAER POLYTECHNIC INSTITUTE   (United States)

Author keywords

Computational cost; Ensemble techniques; Selection; Utility theory; Voting

Indexed keywords

CLASSIFIERS; COMMERCE; COMPUTATIONAL METHODS; COST ACCOUNTING; SELECTION; SET THEORY; UTILITY PROGRAMS;

COMPUTATIONAL COST; ENSEMBLE TECHNIQUES; UTILITY THEORY; VOTING;

CLASSIFICATION (OF INFORMATION);

EID: 25644439038     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.03.028     Document Type: Article

References (16)

1. H. Akaike A new look at the statistical model identification IEEE Trans. Automat. Contr. 19 1974 716 723
2. Alimoglu, F., Alpaydin, E., 1997. Combining multiple representations and classifiers for pen-based handwritten digit recognition. ICDAR1997, Ulm.
3. E. Alpaydin Introduction to Machine Learning 2004 The MIT Press Cambridge, MA
4. L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone Classification and Regression Trees 1984 Wadsworth California
5. Blake, C.L., Merz, C.J., 1998. UCI Repository of machine learning databases. Available from: http://www.ics.uci.edu/~mlearn/MLRepository.html.
6. O.R. Duda, E.P. Hart, and G.D. Stork Pattern Classification 2001 Wiley-Interscience New York
7. G. Giacinto, and F. Roli A theoretical framework for dynamic classifier selection 15th Internat. Conf. Pattern Recognition vol. 2 2000 IEEE Press 8 11
8. M.H. Hansen, and B. Yu Model Selection and the principle of minimum description length J. Amer. Statist. Assoc. 96 2001 746 774
9. R.A. Jacobs, M.I. Jordan, S.J. Nowlan, and G.E. Hinton Adaptive mixtures of local experts Neural Comput. 3 1991 79 87
10. J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas On combining classifiers IEEE Trans. Pattern Anal. 20 1998 226 239
11. L.I. Kuncheva, M. Skurichina, and R.P.W. Duin An experimental study on diversity for bagging and boosting with linear classifiers Inform. Fusion 3 2002 245 258
12. D.V. Lindley Decision Making 1971 Wiley-Interscience New York
13. P.D. Turney Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm J. Artificial Intelligence Res. 2 1995 369 409
14. Turney, P.D., 2000. Types of cost in inductive concept learning. Workshop on Cost-Sensitive Learning, ICML2000, Stanford, pp. 15-21.
15. D.H Wolpert Stacked generalization Neural Networks 5 1992 241 259
16. K. Woods, W.P. Kegelmeyer, and K. Bowyer Combination of multiple classifiers using local accuracy estimators IEEE Trans. Pattern Anal. 19 1997 405 410