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Decision Rule Induction Based on the Graph Theory

In: Application of Decision Science in Business and Management

Author

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  • Izabela Kutschenreiter-Praszkiewicz

Abstract

The graph theory is a well-known and wildly used method of supporting the decision-making process. The present chapter presents an application of a decision tree for rule induction from a set of decision examples taken from past experiences. A decision tree is a graph, where each internal (non-leaf) node denotes a test on an attribute which characterises a decision problem, each branch (also called arc or edge) represents the outcome of a test (attribute value), and each leaf (or terminal) node holds a class label which can be interpreted as a decision type. In the presented approach, the object-attribute-value (OAV) framework will be used for decision problem characteristics. The chapter presents a method of optimal decision tree induction. It discusses the Iterative Dichotomiser 3 (ID3) algorithm and provides an example of the decision tree induction. Also, rules supporting the decision-making in engineering will be developed in this chapter.

Suggested Citation

  • Izabela Kutschenreiter-Praszkiewicz, 2020. "Decision Rule Induction Based on the Graph Theory," Chapters, in: Fausto Pedro Garcia Marquez (ed.), Application of Decision Science in Business and Management, IntechOpen.
    • Handle: RePEc:ito:pchaps:201560
    DOI: 10.5772/intechopen.88737
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    More about this item

    Keywords

    decision tree; decision rule induction; ID3 algorithm; QFD; dependence network;
    All these keywords.

    JEL classification:

    • D7 - Microeconomics - - Analysis of Collective Decision-Making

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