Abstract
In this paper, we introduce a probabilistic rough induction methodology and discuss two algorithms for its implementation. This methodology is based on the combination of Generalization Distribution Table (GDT) and the Rough Set theory (GDT-RS for short). A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. The GDT provides a probabilistic basis for evaluating the strength of a rule. The rough set theory is used to find minimal relative reducts from the set of rules with larger strength. Main features of the GDT-RS are (1) biases can be selected flexibly for search control, and background knowledge can be used as a bias to control the creation of a GDT and the rule induction process; (2) the uncertainty of a rule including the prediction of possible instances can be represented explicitly in the strength of the rule.
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© 1999 Springer-Verlag Berlin Heidelberg
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Dong, J., Zhong, N., Ohsuga, S. (1999). Probabilistic rough induction: The GDT-RS methodology and algorithms. In: Raś, Z.W., Skowron, A. (eds) Foundations of Intelligent Systems. ISMIS 1999. Lecture Notes in Computer Science, vol 1609. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0095151
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DOI: https://doi.org/10.1007/BFb0095151
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