27-08-2012, 04:42 PM
Decision Trees for Uncertain Data
Decision Trees.doc (Size: 51.5 KB / Downloads: 30)
What is Decision Tree?
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
It is one way to display an algorithm.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.
Another use of decision trees is as a descriptive means for calculating conditional probabilities.
A decision tree consists of 3 types of nodes:-
1. Decision nodes - commonly represented by squares
2. Chance nodes - represented by circles
3. End nodes - represented by triangles
Example
Decision trees can be used to optimize an investment portfolio.
The following example shows a portfolio of 7 investment options (projects).
The organization has $10,000,000 available for the total investment.
Bold lines mark the best selection 1, 3, 5, 6, and 7, which will cost $9,750,000 and create a payoff of 16,175,000.
What is uncertain Data?
In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values.
Decision Trees.doc (Size: 51.5 KB / Downloads: 30)
What is Decision Tree?
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
It is one way to display an algorithm.
Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal.
Another use of decision trees is as a descriptive means for calculating conditional probabilities.
A decision tree consists of 3 types of nodes:-
1. Decision nodes - commonly represented by squares
2. Chance nodes - represented by circles
3. End nodes - represented by triangles
Example
Decision trees can be used to optimize an investment portfolio.
The following example shows a portfolio of 7 investment options (projects).
The organization has $10,000,000 available for the total investment.
Bold lines mark the best selection 1, 3, 5, 6, and 7, which will cost $9,750,000 and create a payoff of 16,175,000.
What is uncertain Data?
In computer science, uncertain data is the notion of data that contains specific uncertainty. Uncertain data is typically found in the area of sensor networks. When representing such data in a database, some indication of the probability of the various values.