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The primary thinking processes, as codified by Goldratt and others: Current reality tree (CRT, similar to the current state map used by many organizations) — evaluates the network of cause-effect relations between the undesirable effects (UDE's, also known as gap elements) and helps to pinpoint the root cause(s) of most of the undesirable effects.
In 1988, David Hyerle wrote Expand Your Thinking and introduced Thinking Maps. These are a set of techniques used in primary and secondary education with the intention of providing a common visual language to information structure. There are eight types of maps: Circle Map: used for defining in context; Bubble Map: used for describing with ...
The use of such argument analysis for thinking through issues has been called "reflective argumentation". [15] An argument map, unlike a decision tree, does not tell how to make a decision, but the process of choosing a coherent position (or reflective equilibrium) based on the structure of an argument map can be represented as a decision tree ...
One of the thinking processes in the theory of constraints, a current reality tree (CRT) is a tool to analyze many systems or organizational problems at once. By identifying root causes common to most or all of the problems, a CRT can greatly aid focused improvement of the system. A current reality tree is a directed graph.
An issue tree, also called logic tree, is a graphical breakdown of a question that dissects it into its different components vertically and that progresses into details as it reads to the right. [1]: 47 Issue trees are useful in problem solving to identify the root causes of a problem as well as to identify its potential solutions. They also ...
The original tree is converted to a binary tree: each node with more than two children is replaced by a sub-tree in which each node has exactly two children. Each region representing a node (starting from the root) is divided to two, using a line that keeps the angles between edges as large as possible.
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The left tree is the decision tree we obtain from using information gain to split the nodes and the right tree is what we obtain from using the phi function to split the nodes. The resulting tree from using information gain to split the nodes. Now assume the classification results from both trees are given using a confusion matrix.