Abstract
The use of classification trees in two quite different application areas –business documents on one side and geographic information systems on the other– is presented. What is in common between such so different applications of the classification techniques based on trees is the need of complementing the straightforward use of induction with the exploitation of some form of deductive, or better to say expert, knowledge. When working on business documents, the expert knowledge, in the form of rules elicited from human experts, is used to improve the construction of the classification tree by complementing the inductive knowledge coming from the examples in the choice of the next node to add to the tree. When working on geographic information systems, the expert knowledge, in the form of specifying which are the spatial relationships among the geographic objects, is used to extract the information from the GIS in a form that can be then processed in an inductive style.
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References
Baglioni, M., Furletti, B., Turini, F.: Drc4.5: Improving c4.5 by means of prior knowledge. In: Proocedings of the 2005 ACM Symposium on applied computing, pp. 474–481 (2005)
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks (1984)
BRITE, http://www.briteproject.net/
Clementini, E., Di Felice, P., Oosterorn, O.: A small set of formal topological relationships suitable for end-user interaction. In: Abel, D.J., Ooi, B.-C. (eds.) SSD 1993. LNCS, vol. 692. Springer, Heidelberg (1993)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1972)
Eden, C., Ackermann, F., Copper, S.: The analysis of cause maps. Journal of Management Studies 29(3), 309–323 (1992)
GeoPKDD, http://geopkdd.isti.cnr.it/
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Kemmerer, B., Mishra, S., Shenoy, P.P.: Bayesian causal maps as decision aids in venture capital decision making: Methods and applications. In: Proceedings of the Accademy of Management Conference (2002)
Loh, W.-Y., Shih, Y.-S.: Split selection methods for classification trees. Statistica Sinica (1997)
Loh, W.Y., Vanichsetakul, N.: Tree-structured classification via generalized discriminant analysis. Journal of the American Statistical Association 83, 715–728 (1988)
Mitchell, T.M.: Machine Learning. McGraw-Hil, New York (1997)
MUSING, http://musing.metaware.it/
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1) (1986); QUINLAN 1986
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1992)
Rinzivillo, S., Turini, F.: Classification in geographical information system. In: 8th European Conference on Principles and Practice of Knowledfe Discovery in Databases (2004)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by back-propagating errors. In: Rumelhart, D.E. (ed.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, MA. Bradford Books (1986)
Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn, Classification and Prediction Methods from Statistics, Neural Networks, Machine Learning and Expert Systems. Morgan Kaufmann, San Mateo (1991)
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Turini, F., Baglioni, M., Furletti, B., Rinzivillo, S. (2006). Examples of Integration of Induction and Deduction in Knowledge Discovery. In: Stock, O., Schaerf, M. (eds) Reasoning, Action and Interaction in AI Theories and Systems. Lecture Notes in Computer Science(), vol 4155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11829263_17
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DOI: https://doi.org/10.1007/11829263_17
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