Statistical Science

Elaboration on Two Points Raised in “Classifier Technology and the Illusion of Progress”

Robert C. Holte
Source: Statist. Sci. Volume 21, Number 1 (2006), 24-26.
First Page: Show Hide
Full-text: Open access
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.ss/1149600842
Digital Object Identifier: doi:10.1214/088342306000000033

References

Auer, P., Holte, R. C. and Maass, W. (1995). Theory and applications of agnostic PAC-learning with small decision trees. In Proc. Twelfth International Conference on Machine Learning 21--29. Morgan Kaufmann, San Francisco.
Domingos, P. and Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero--one loss. Machine Learning 29 103--130.
Drummond, C. and Holte, R. C. (2000). Explicitly representing expected cost: An alternative to ROC representation. In Proc. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 198--207. ACM Press, New York.
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 63--90.
Kohavi, R. (1995). The power of decision tables. In Proc. Eighth European Conference on Machine Learning. Lecture Notes in Artificial Intelligence 912 174--189. Springer, Berlin.
Michie, D., Spiegelhalter, D. J. and Taylor, C. C., eds. (1994). Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York.
Newman, D. J., Hettich, S., Blake, C. L. and Merz, C. J. (1998). UCI repository of machine learning databases. Dept. Information and Computer Sciences, Univ. California, Irvine. Available at www.ics.uci.edu/~mlearn/MLRepository.html.
Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA.
Zentralblatt MATH: 0900.68112
Shavlik, J., Mooney, R. J. and Towell, G. (1991). Symbolic and neural learning algorithms: An experimental comparison. Machine Learning 6 111--143.
Zentralblatt MATH: 1141.68327
Webb, G. and Ting, K. M. (2005). On the application of ROC analysis to predict classification performance under varying class distributions. Machine Learning 58 25--32.

2012 © Institute of Mathematical Statistics

Statistical Science

Statistical Science