Modern analytical tasks in biomedical research require increasingly sophisticated methods of data analysis. In recent years, the term analysis is not only related to classical statistical tests for hypothesis testing and correlation studying associations between variables. Classification tree or decision getting more frequently used research. this paper we present use classification trees provi...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Random forest can achieve high classification performance through a classification ensemble with a set of decision trees that grow using randomly selected subspaces of data. The performance of an ensemble learner is highly dependent on the accuracy of each component learner and the diversity among these components. In random forest, randomization would cause occurrence of bad trees and may incl...

Model-based development uses modeling and simulation as essential means for specification, rapid prototyping, design, and realization of embedded systems. The classification-tree method complements model-based development with a formal approach for test case description and automation. This paper shows how “raw” classification trees are transformed into complete classification trees using an ex...

The classification-tree method developed by Grochtmann and Grimm facilitates the identification of test cases from functional specifications via the construction of classification trees. Their method has been enhanced by Chen and Poon through the classification-tree construction and restructuring methodologies. We find, however, that the restructuring algorithm by Chen and Poon is applicable on...

Decision tree learning is one of the most popular classification techniques. However, by its nature it is a greedy approach to finding a classification hypothesis that optimizes some information-based criterion. It is very fast but may lead to finding suboptimal classification hypotheses. Moreover, in spite of decision trees being easily interpretable, ensembles of trees (random forests and gra...