Abstract
Many machine learning tasks contain feature evaluation as one of its important components. This work is concerned with attribute estimation in the problems where class distribution is unbalanced or the misclassification costs are unequal. We test some common attribute evaluation heuristics and propose their cost-sensitive adaptations. The new measures are tested on problems which can reveal their strengths and weaknesses.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth Inc., Belmont (1984)
Dietterich, T.G., Kerns, M., Mansour, Y.: Applying the weak learning framework to understand and improve C4.5. In: Saitta, L. (ed.) Machine Learning: Proceedings of the Thirteenth International Conference (ICML 1996), pp. 96–103. Morgan Kaufmann, San Francisco (1996)
Drummond, C., Holte, R.C.: Exploiting the cost (in)sensitivity of decision tree splitting criteria. In: Proceedings of the Seventeenth International Conference on Machine Learnintg (ICML 2000), pp. 239–246 (2000)
Elkan, C.: The foundations of cost-sensitive learning. In: Proceedings of the Seventeenth International Joint Conference on Artificaial Intelligence, IJCAI 2001 (2001)
Kira, K., Rendell, L.A.: A practical approach to feature selection. In: Sleeman, D., Edwards, P. (eds.) Machine Learning: Proceedings of International Conference (ICML 1992), pp. 249–256. Morgan Kaufmann, San Francisco (1992)
Kononenko, I.: Estimating attributes: analysis and extensions of Relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
Kononenko, I.: On biases in estimating multi-valued attributes. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAF 1995), pp. 1034–1040. Morgan Kaufmann, San Francisco (1995)
Kukar, M., Kononenko, I., Grošelj, C., Kralj, K., Fettich, J.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine 16, 25–50 (1999)
Margineantu, D.D.: On class-probability estimates and cost-sensitive evaluation of classifiers. In: Workshop on Cost-Sensitive Learning at the Seventeenth International Conference on Machine Learning (WCSL at ICML 2000) (2000)
Margineantu, D.D., Dietterich, T.G.: Bootstrap methods for the cost-sensitive evaluation of classifiers. In: Machine Learning: Proceedings of Seventeenth International Conference on Machine Learning (ICML 2000), pp. 583–590. Morgan Kaufmann, San Francisco (2000)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Robnik-Sikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Machine Learning Journal (2003), http://www.kluweronline.com/issn/0885-6125/ , (forthcoming, available also as technical report at http://lkm.fri.uni-lj.si/rmarko/ )
Turney, P.D., Boz, O.: On-line cost-sensitive learning bibliography (1996-2001), http://home.ptd.net/olcay/cost-sensitive.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Robnik-Šikonja, M. (2003). Experiments with Cost-Sensitive Feature Evaluation. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds) Machine Learning: ECML 2003. ECML 2003. Lecture Notes in Computer Science(), vol 2837. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39857-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-39857-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20121-2
Online ISBN: 978-3-540-39857-8
eBook Packages: Springer Book Archive