Decision nodes

There are two types of nodes in the decision tree decision nodes (rectangular) and chance nodes (circular). Decision nodes branch into a set of possible actions, while chance nodes branch into all possible results or situations. 

For decision nodes, it is assumed that good management will lead us to decide on the action which will result in the highest NPV. Hence the value of the decision node is the optimum of the values of its actions. 

Figure 5. A decision tree for the choice of sample size and inference method. The first decision node represents the choice of the sample size re. After this decision, the experiment is conducted and generates the data y that are assumed to follow a distribution with parameter 6. The data are used to make an inference on the parameter 6, and the second decision node a represents the statistical procedure that is used to make this inference. The last node represents the loss induced by choosing such an experiment.

The decision problem is represented by the decision tree in Figure 5, in which open circles represent chance nodes, squares represent decision nodes, and the black circle is a value node. The first decision node is the selection of the sample size n used in the experiment, and c represents the cost per observation. The experiment will generate random data values y that have to be analyzed by an inference method a. The difference between the true state of nature, represented by the fold changes 6 = 9, 9g), and the inference will determine a loss L(-) that is a function of the two decisions n and a, the data, and the experimental costs. There are two choices in this decision problem the optimal sample size and the optimal inference. 

The solutions are found by averaging out and folding back (Raiffa and Schlaifer, 1961), so that we compute the expected loss at the chance nodes (open circles), given everything to the left of the node. We determine the best actions by minimizing the expected loss at the decision nodes. The first decision is the choice of the inference method a and the optimal decision a (or Bayes action) is found by minimizing the expected loss E L(n, 6, y, a, c), where the expectation is with respect to the conditional distribution of 0 given n and y. The expected loss evaluated in the Bayes action a is called the Bayes risk and we denote it by... 

Many expert systems contain a knowledge base in the form of a decision tree that is constructed from a series of decision nodes connected by branches. For instance, in expert systems developed for the interpretation of vibrational spectra, decision trees are typically used in a sequential manner. Similar to the interpretation of a spectrum... 

The Rete algorithm stores not just exact matches but also partial matches this avoids reevaluating the complete set of facts when the rule base is changed. Additionally, it uses a decision-node-sharing technique, which eliminates certain redundancies. 

During training, each decision node of the tree creates a set of random tests and then selects the best according to quality measurement such as information gain or Gini index. The trees are usually grown to their full size without pruning. We denote the nth tree of the ensemble as... 

We then construct the corresponding BBN, shown in Fig.3. Note that in the network, technique-related factors influence verifiers potential while verifiers potential, and the rest types of factors influence V V effectiveness. The rectangles in Fig. 3 are decision nodes the diamond nodes represent costs of different activities. 

The decision tree classifier is chosen for its favorable tradeoff between performance and implementation simplicity. Classification using DT is a supervised learning technique, the input of the learning algorithm is a set of known data and the output is a tree model similar to the ones shown in Figure 5. Once the tree is defined, the classification of new inputs starts at the root decision node of the tree and terminates at one of the leaf nodes that represent a specific class, passing by intermediate decision nodes. 

D = [D. .. Dm is a set of decision nodes which depict decision options. These nodes should respect a temporal order. Decision nodes are represented by rectangles. 

Arcs in A have different meanings according to their targets. We can distinguish conditional arcs (into chance and value nodes), those that have as target chance nodes represent probabUis-tic dependencies and informational arcs (into decision nodes) which imply time precedence. 

Influence diagrams are required to satisfy some constraints to be regular, in particular value nodes cannot have children and there is a directed path that contains all of the decision nodes. As a result of this last constraint, influence diagrams will satisfy the o- /org-efrt g-property in the sense that a decision node and its parents should be parents to all subsequent decision nodes. 

If there is a decision node which is a direct predecessor of the value node such that the remaining predecessors of the value node are informational predecessors of the decision node, then ... 

Find a chance node i which is a direct predecessor to the value node such that it has no decision node as successor. 

D= PMKPME, ETT, PPT, FS, FTC, BPW, PPET, PPETO, EFP, SIN, ST where aU decision nodes are binary (i.e. can take True (T) or False (F) ) except ST and FS which are ternary (i.e. can take Rare (R), Moderate (M) or Frequent (F)). Note, that the no-forgetting arcs between decision nodes are not represented in figure 5. 

Then, we should proceed to the quantification phase. For the lack of space we cannot give numerical data here (for instance the table relative to Vqse contains 10 4-2 entries since we have 10 binary and 2 ternary decision nodes ). Once the transformation achieved, we can apply the evaluation algorithm (i.e. Algorithm 1) proposed by (Micheal and Yacov 2004). 

The committee used the decision tree (Figure 3.1) to determine whether individual capabilities should be maintained by the program and where. Many of the metrics used to address each decision node on the tree are subjective and the committee s consensus view is described in this chapter. If the CBDP does not agree with an individual assessment of an S T capability, they are encouraged to undertake a de novo analysis of that capability, using the decision tree above, to reach their own conclusion. 

Decision tree technique A typical decision tree is shown in Fig. 11/4.3.8-1. Here there are two major nodes one is the decision node and the other is the probabilistic or chance node. The figure shows the decision regarding cost versus risk. 

Activity Node Decision Node o Initiation/Temiination... 

A Bayesian network was developed for verification and comparison of achieved result by event tree method. The network was developed in program Genie and contains 5 random nodes, 5 utility nodes, and 2 decision nodes. 

There are two decision nodes in the presented study—the Intensity of traffic and the proposed type of the road Safety barriers. The decision node Intensity of traffic " has three states. 

The decision node "Type of safety barriers"" has eight states, first state No barriers is unprotected surrounding. Other seven states cover classes of... 

Belief decision tree is the combination of standard decision tree and belief fimctions. Basic elements of a belief decision tree consisted of a decision node, an edge, and a leaf with uncertain label. Averaging approach was selected to define the attributes selection measures based on extended gain criterion of the information theoiy of Shannon. A mean value was used as partitioning strategy. The growth process of belief decision tree was stopped when there are no more attributes to be tested [13]. 

---