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2017
This chapter provides further detail and depth on the construction of decision trees. Properties of expected value and decision trees are presented along with examples that demonstrate the use of probability and expected value of perfect and imperfect information.
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This chapter provides further detail and depth on the construction of decision trees. Properties of expected value and decision trees are presented along with examples that demonstrate the use of probability and expected value of perfect and imperfect information.
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Management Decision, 1995
Explores, using appropriate examples, the ways in which decision trees can be used by the manager to assist in the longitudinal decision‐making process. Since the mathematical concepts associated with decision trees are complex, managers can be reluctant to attempt to use decision tree models. A recognition that such models can be simply developed in a
Coles, Susan, Rowley, Jennifer
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Explores, using appropriate examples, the ways in which decision trees can be used by the manager to assist in the longitudinal decision‐making process. Since the mathematical concepts associated with decision trees are complex, managers can be reluctant to attempt to use decision tree models. A recognition that such models can be simply developed in a
Coles, Susan, Rowley, Jennifer
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Collegian, 2000
Financial cutbacks and budgetary constraints continue to be a part of government policy, and impact deeply on many aspects of daily life in Australia in the late 90s. Health care is not immune from these measures, and nurses and other allied health professionals are having to manage increasingly complex patient care with less funding and resources ...
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Financial cutbacks and budgetary constraints continue to be a part of government policy, and impact deeply on many aspects of daily life in Australia in the late 90s. Health care is not immune from these measures, and nurses and other allied health professionals are having to manage increasingly complex patient care with less funding and resources ...
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Decision Trees and Applications
2020In many cases, the meaning of information is wrongly related to either the sense of data or the notion of knowledge. There is a crucial sequence of steps before information becomes knowledge and the value of data depends in the existence of information so as to produce knowledge.
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2010
Consider a wireless sensor network in which each node possesses a bit of information. Suppose all sensors with the bit 1 broadcast this fact to a central processor. If zero or one sensors broadcast, the central processor can detect this fact. If two or more sensors broadcast, the central processor can only detect that there is a "collision." Although ...
Steve Uurtamo +5 more
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Consider a wireless sensor network in which each node possesses a bit of information. Suppose all sensors with the bit 1 broadcast this fact to a central processor. If zero or one sensors broadcast, the central processor can detect this fact. If two or more sensors broadcast, the central processor can only detect that there is a "collision." Although ...
Steve Uurtamo +5 more
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Decision trees and decision-making
IEEE Transactions on Systems, Man, and Cybernetics, 1990Various practical systems capable of extracting descriptive decision-making knowledge from data have been developed and evaluated. Techniques that represent knowledge about classified tasks in the form of decision trees are examined. A sample of techniques is sketched, ranging from basic methods of constructing decision trees to ways of using them ...
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2008
The first (crisp) decision tree techniques were introduced in the 1960s (Hunt, Marin, & Stone, 1966), their appeal to decision makers is due in no part to their comprehensibility in classifying objects based on their attribute values (Janikow, 1998).
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The first (crisp) decision tree techniques were introduced in the 1960s (Hunt, Marin, & Stone, 1966), their appeal to decision makers is due in no part to their comprehensibility in classifying objects based on their attribute values (Janikow, 1998).
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Decision analysis by decision tree
Omega, 1973Abstract The process of decision analysis by decision tree for any decision maker may essentially be summarized as follows: 1. (a) Form the decision tree for the problem; 2. (b) Quantify your judgement by assigning subjective probabilities to the possible outcomes in the decision tree; 3.
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