1994 | OriginalPaper | Buchkapitel
Minimizing decision table sizes in influence diagrams: dimension shrinking
verfasst von : Nevin Lianwen Zhang, Runping Qi, David Poole
Erschienen in: Selecting Models from Data
Verlag: Springer New York
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
One goal in evaluating an influence diagram is to compute an optimal decision table for each decision node. More often than not, one is able to shrink the sizes of some of the optimal decision tables without any loss of information. This paper investigates when the opportunities for such shrinkings arise and how can we detect them as early as possible so as to to avoid unnecessary computations. One type of shrinking, namely dimension shrinking, is studied. A relationship between dimension shrinking and what we call lonely arcs is established, which enables us to make use of the opportunities for dimension shrinking by means of pruning lonely arcs at a preprocessing stage.