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Learning Classification Rules from Database in the Context of Knowledge-Acquisition and -Representation

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Intelligent Decision Support

Part of the book series: Theory and Decision Library ((TDLD,volume 11))

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Abstract

The bottle-neck of manual knowledge-acquisition is a major obstacle for knowledge representation. The research described in this paper addresses this problem by suggesting a method for learning knowledge from a database. We attempt to improve representation with the assistance of experts and from computer resident knowledge. We describe the knowledge representation in the framework of a conceptual schema consisting of a semantic model and an event model. In these models a concept classifies a domain into different sub-domains. As a method of knowledge acquisition, we apply inductive learning techniques for rule generation. The theory of Rough Sets is used in designing the learning algorithm. Examples of certain concepts are used to induce general specifications of the concepts called classification rules. The basic approach here is to partition the information into equivalence classes, and derive conclusions based on equivalence relations. In a sense we are involved in a data-reduction process, where we want to reduce a large database of information to a small number of rules describing the domain. This is a completely integrated approach that includes user interface, semantics, constraints, representation of temporal events, induction, etc.

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© 1992 Springer Science+Business Media Dordrecht

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Yasdi, R. (1992). Learning Classification Rules from Database in the Context of Knowledge-Acquisition and -Representation. In: Słowiński, R. (eds) Intelligent Decision Support. Theory and Decision Library, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7975-9_26

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  • DOI: https://doi.org/10.1007/978-94-015-7975-9_26

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4194-4

  • Online ISBN: 978-94-015-7975-9

  • eBook Packages: Springer Book Archive

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