Decision Rule Induction Based on the Graph Theory

Izabela Kutschenreiter-Praszkiewicz

A chapter in Application of Decision Science in Business and Management from IntechOpen

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.

Keywords: decision tree; decision rule induction; ID3 algorithm; QFD; dependence network (search for similar items in EconPapers)
JEL-codes: D7 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:201560

DOI: 10.5772/intechopen.88737

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