A probabilistic approach for reducing the search cost in binary decision trees

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dc.contributor.authorRontogiannis, Athanasiosen_US
dc.date.accessioned2024-08-15T17:41:33Z
dc.date.available2024-08-15T17:41:33Z
dc.date.copyright1993en_US
dc.date.issued1993
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Science M.Sc.en
dc.description.abstractIn many complex problems a particular decisionmaking procedure is often required in order for a final solution to be found. Such a procedure may consist of a large number of intermediate steps where "local" decisions must be taken and can be sometimes represented as a decision tree. When that structure is used the final solutions obtained vary depending on the available information. However, if the same model is applied many times, experimental data can be collected and observations on the acquired knowledge can be made. In this work, we present a probabilistic approach for reducing the number of decisions (tests) that are required in a particular decisionmaking situation. Specifically, we consider that a problem is structured as a decision binary balanced tree the interior nodes of which correspond to decision points; the paths of the tree represent different deci­sionmaking processes. By assuming that there exists sufficient probabilistic information concerning the decisions at the decision nodes, we attempt to mini­mize the average number of these decisions when we search for a final solution.en
dc.format.extent102 pages
dc.identifier.urihttps://hdl.handle.net/1828/19507
dc.rightsAvailable to the World Wide Weben_US
dc.subjectUN SDG 16: Peace, Justice, and Strong Institutionsen
dc.titleA probabilistic approach for reducing the search cost in binary decision treesen_US
dc.typeThesisen_US

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