Abstract
In machine learning many real-life applications data are characterized by attributes with unknown values. This paper shows that the existing approaches to learning from such examples are not sufficient. A new method is suggested, which transforms the original decision table with unknown values into a new decision table in which every attribute value is known. Such a new table, in general, is inconsistent. This problem is solved by a technique of learning from inconsistent examples, based on rough set theory. Thus, two sets of rules: certain and possible are induced. Certain rules are categorical, while possible rules are supported by existing data, although conflicting data may exist as well. The presented approach may be combined with any other approach to uncertainty when processing of possible rules is concerned.
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T. Arciszewski, M. Mustafa, and W. Ziarko. A methodology of design knowledge acquisition for use in learning expert systems. Int. J. Man-Machine Studies 27, 1987, 23–32.
J. Catlett. Induction using the Shafer representation. Technical Report. Basser Department of Computer Science, University of Sydney, Australia, 1985.
C.-C. Chan and J. W. Grzymala-Busse. Rough-set boundaries as a tool for learning rules from examples. Proc. ISMIS-89, 4th Int. Symposium on Methodologies for Intelligent Systems, 1989, 281–288.
P. Clark, T. Niblett. The CN2 induction algorithm. Machine Learning 3, 1989, 261–283.
T. G. Dietterich, R. S. Michalski. A comparative review of selected methods for learning from examples. In Machine Learning. An Artificial Intelligence Approach, ed. R. S. Michalski, J. G. Carbonell, T. M. Mitchell, Morgan Kauffman, 1983, 41–81.
J. H. Friedman. A recursive partitioning decision rule for nonparametric classification. IEEE Transactions on Computers, 1977, 404–408.
J. W. Grzymala-Busse. Knowledge acquisition under uncertainty—a rough set approach. Journal of Intelligent & Robotic Systems 1, 1988, 3–16.
I. Knonenko, I. Bratko, E. Roskar. Experiments in automatic learning of medical diagnostic rules. Technical Report, Jozef Stefan Institute, Ljubljana, Yugoslavia, 1984.
D. Maier. The Theory of Relational Databases, Computer Science Press, 1983.
M. V. Manago, Y. Kodratoff. Noise and knowledge acquisition. Proc. IJCAI 1987, 348–354.
R. S. Michalski. A theory and methodology of inductive learning. In Machine Learning. An Artificial Intelligence Approach, ed. R. S. Michalski, J. G. Carbonell, T. M. Mitchell, Morgan Kauffman, 1983, 83–134.
Z. Pawlak. Rough sets. Int. J. Computer and Information Sci. 11, 1982, 341–356.
J. R. Quinlan. Induction of decision trees. Machine Learning 1, 1986, 81–106.
J. R. Quinlan. Decision trees as probabilistic classifiers. Proc. 4th Int. Workshop on Machine Learning 1987, 31–37.
J. R. Quinlan. Unknown attribute values in induction. Proc. 6th Int. Workshop on Machine Learning, 1989, 164–168.
J. R. Quinlan. Probabilistic decision trees. In Machine Learning. An Artificial Intelligence Approach, vol III, ed. Y. Kodratoff and R. S. Michalski, 1990, 140–152.
R. Yasdi and W. Ziarko. An expert system for conceptual schema design: A machine learning approach. Int. J. Man-Machine Studies 29, 1988, 351–376.
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© 1991 Springer-Verlag Berlin Heidelberg
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Grzymala-Busse, J.W. (1991). On the unknown attribute values in learning from examples. In: Ras, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1991. Lecture Notes in Computer Science, vol 542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54563-8_100
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DOI: https://doi.org/10.1007/3-540-54563-8_100
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