Cogprints
Login | Create Account

Types of cost in inductive concept learning

Turney, Peter (2000) Types of cost in inductive concept learning. [Conference Paper] (Unpublished)

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
47Kb
[img]
Preview
Postscript - Requires a viewer, such as GSview
515Kb

Abstract

Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth.

Item Type:Conference Paper
Keywords:cost, learning, misclassification error, inductive concept learning, complexity, cost-sensitive learning.
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1804
Deposited By:Turney, Peter
Deposited On:17 Sep 2001
Last Modified:12 Sep 2007 17:40

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees. California: Wadsworth.

Cohn, D.A., Ghahramani, Z., and Jordan, M.I. (1995). Active learning with statistical models. In Tesauro, G., Touretzky, D., and Leen, T. (eds.), Advances in Neural Information Processing Systems 7, pp. 705-712. MIT Press, Cambridge, MA.

Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4 , 129-145.

Domingos, P. (1998). Knowledge discovery via multiple models. Intelligent Data Analysis, 2: 187-202.

Fawcett, T. (1993). Feature Discovery for Problem Solving Systems. Doctoral dissertation, Department of Computer Science, University of Massachusetts, Amherst, MA.

Fawcett, T., and Provost, F.J. (1996). Combining data mining and machine learning for effective user profiling. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD-96, pp. 8-13.

Fawcett, T., and Provost, F.J. (1997). Adaptive fraud detection. Data Mining and Knowledge Discovery, 1 (3).

Fawcett, T., and Provost, F.J. (1999). Activity monitoring: Noticing interesting changes in behavior. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, KDD-99.

Hasenjager, M., and Ritter, H. (1998). Active learning with local models. Neural Processing Letters, 7(2), 107-117.

Hermans, J., Habbema, J.D.F., and Van der Burght, A.T. (1974). Cases of doubt in allocation problems, k populations. Bulletin of the International Statistics Institute, 45, 523-529.

Krogh, A., and Vedelsby, J. (1995). Neural network ensembles, cross validation, and active learning, Neural Information Processing Systems 7, pp. 231-238. MIT Press.

Mingers, J. (1989). An empirical comparison of pruning measures for decision tree induction. Machine Learning, 4: 227-243.

Opitz, D.W., and Shavlik, J.W. (1997). Connectionist theory refinement: Genetically searching the space of network topologies. Journal of Artificial Intelligence Research, 6: 177-209.

Núñez, M. (1988). Economic induction: A case study. Proceedings of the Third European Working Session on Learning, EWSL-88, pp. 139-145. California: Morgan Kaufmann.

Núñez, M. (1991). The use of background knowledge in decision tree induction. Machine Learning, 6, 231-250.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. California: Morgan Kaufmann.

Pipitone, F., De Jong, K.A., and Spears, W.M. (1991). An artificial intelligence approach to analog systems diagnosis. In Testing and Diagnosis of Analog Circuits and Systems, Ruey-wen Liu, editor. New York: Van Nostrand-Reinhold.

Provost, F.J., Jensen, D., and Oates, T. (1999). Efficient progressive sampling. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, KDD-99.

Tan, M. (1991a). Cost-sensitive reinforcement learning for adaptive classification and control. Proceedings of the Ninth National Conference on Artificial Intelligence, 774-780. San Jose, CA: AAAI Press.

Tan, M. (1991b). Learning a cost-sensitive internal representation for reinforcement learning. Proceedings of the Eighth International Workshop on Machine Learning, 358-362. Evanston, IL: Morgan Kaufmann.

Tan, M. (1993). Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning, 13, 7-33.

Turney, P.D. (1995a). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research, 2, 369-409.

Turney, P.D. (1995b). Low size-complexity inductive logic programming: The East-West Challenge considered as a problem in cost-sensitive classification. In Proceedings of the Fifth International Inductive Logic Programming Workshop, 247-263.

Turney, P.D. (1995c). Bias and the quantification of stability. Machine Learning, 20: 23-33.

Turney, P.D., Schaffer, C., and Holte, R. (1995). Editors. Proceedings of the IJCAI-95 Workshop on Data Engineering for Inductive Learning. Montréal, Canada. (http://www.iit.nrc.ca/DEIL/).

van Rijsbergen, C.J. (1979). Information Retrieval (2nd edition), Butterworths, London.

van Someren, M.W., Torres, C., and Verdenius, F. (1997). A systematic description of greedy optimisation algorithms for cost sensitive generalisation. Proceedings of Intelligent Data Analysis 1997 (IDA97), Springer Verlag, New York, pp. 247-258.

Verdenius, F. (1991). A method for inductive cost optimization. Proceedings of the Fifth European Working Session on Learning, EWSL-91, pp. 179-191. New York: Springer-Verlag.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.