Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questio...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Quinlan, J. R. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Cambridge, Mass. 1986
Schriftenreihe:	Artificial Intelligence Laboratory <Cambridge, Mass.>: A. I. Memo 930.
Schlagworte:	Decision Trees Accuracy Artificial intelligence Cybernetics Decision making Decision theory Fault tree analysis Information processing Operations Research Test beds Künstliche Intelligenz
Zusammenfassung:	Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the disadvantage of excessive complexity that can render them incomprehensible to experts. It is questionable whether opaque structures of this kind can be described as knowledge, no matter how well they function. This paper discusses techniques for simplifying decision trees without compromising their accuracy. Four methods are described, illustrated, and compared on a test-bed of decision trees from a variety of domains.
Beschreibung:	16 S.