Abstract
We present a new classification algorithm that combines three properties: It generates decision trees, which proved a valuable and intelligible tool for classification and generalization of data; it utilizes fuzzy logic, that provides for a fine grained description of classified items adequate for human reasoning; and it is incremental, allowing rapid alternation of classification and learning of new data. The algorithm generalizes known non-incremental algorithms for top down induction of fuzzy decision trees, as well as known incremental algorithms for induction of decision trees in classical logic. The algorithm is shown to be terminating and to yield results equivalent to the non-incremental version.
This author acknowledges support by hybris GmbH, Munich, within the project “Intelligent Systems in e-Commerce” (ISeC).
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References
C.L. Blake and C.J. Merz. UCI repository of machine learning databases, 1998.
Sven-Erik Bornscheuer, Yvonne McIntyre, Steffen Hölldobler, and Hans-Peter Störr. User adaptation in a Web shop system. In M. H. Hamza, editor, Proceedings of the IASTED International Conference Internet and Multimedia Systems and Applications, pages 208–213, Anaheim, Calgary, Zurich, 2001. ACTA Press.
L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth & Brooks Advanced Books and Software, Pacific Grove, CA, 1984.
D. Dubois and H. Prade. Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, 1980.
Marina Guetova. Inkrementelle Fuzzy-Entscheidungsbäume. Diplomarbeit, Knowledge Representation and Reasoning Group, Department of Computer Science, Technische Universität Dresden, Dresden, Germany, January 2001, (in German).
Tanja Hölldobler. Temporäre Benutzermodellierung für multimediale Produktpräsentationen im World Wide Web. Peter Lang Europäischer Verlag der Wissenschaften, Frankfurt, 2001, (in German).
Cezary Z. Janikow. Fuzzy decision trees: Issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, 28(1):1–14, 1998.
S. K. Murthy. Automatic construction of decision trees from data: a multi-disciplinary survey. Data Mining and Knowledge Discovery, 2:345–389, 1998.
J. R. Quinlan. Induction on decision trees. Machine Learning, 1:81–106, 1986.
J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
Paul E. Utgoff, Neil C. Berkman, and Jeffery A. Clouse. Decision tree induction based on efficient tree restructuring. Machine Learning, 29:5–44, 1997.
L. A. Zadeh. Fuzzy sets. Information and Control, 8:407–428, 1965.
Jens Zeidler. Unscharfe Entscheidungsbäume. PhD thesis, Technische Universität Chemnitz, Fakultät Informatik, 1999.
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Guetova, M., Hölldobler, S., Störr, HP. (2002). Incremental Fuzzy Decision Trees. In: Jarke, M., Lakemeyer, G., Koehler, J. (eds) KI 2002: Advances in Artificial Intelligence. KI 2002. Lecture Notes in Computer Science(), vol 2479. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45751-8_5
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DOI: https://doi.org/10.1007/3-540-45751-8_5
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