The results obtained in this paper by using Decision Tree
Algorithm are compared to the results provided by GA MATLAB toolbox, in a real-time case.
Experiments were performed to obtain the required input data for developing a prediction method based on decision tree
A method of choosing multiway partitions for classification and decision trees
A decision tree
is built for every minimal cut set, refer to Figure 4.
Characterization of variable importance measures derived from decision trees
. Doctoral dissertation, Universite de Liege, Liege, Belgique.8-12.
Keywords: machine learning, decision trees
, naive Bayesian classifier, ReliefF
Logical rule-based methods are decision trees
. Models based on mathematical functions include neural networks (supervised learning network) and parametric or semi-parametric models (linear regression, logistics regression, log-linear model generalized linear model, and so on).
In the following parts, results of decision trees
method will be discussed.
Random forest is a bagging integrated classifier with CART decision trees
as weak classifiers.
The linear methods used include the chi-square, logical regression, decision tree
, linear discriminant analysis, and Native Bayes methods.
Given each division's investment opportunities and how risk factors affect payoffs, each division's decision-making process can be represented in a decision tree
. Figure 4 shows a representative segment of the decision trees
for divisions 1 and 2.