The classification accuracy of decision trees generated by the program is approximately 95%.
We introduced a meta characteristic measure AMfDS (herein known as Affinity Metric for Decision Stump) which is quite useful in prediction of classification accuracy of Decision Stump.
For reference, we also include the classification accuracy of decision trees (DT), Naive Bayes (NB), k-nearest neighbor (k-NN), Support Vector Machines (SVM) and prediction analysis of microarrays (PAM) in our comparison tables when they are available from the literature.
The third phase uses the collective decision table in order to infer a set of collective decision rules, which synthesize the judgements and perspectives of the different decision makers and to permit the classification of all decision objects.
A literature review conducted by Guerreiro [ 11] made an interesting classification of hierarchical decision levels.
The paper then provides a classification of planning decisions, which is used to structure a comprehensive and comparative literature review of the field of semi-flexible systems, including methodological contributions as well as a number of particularly significant practical experiences.
These include the analysis of common signature purposes in workflow, classification of these purposes, classification of modes of decision making associated with these signature purposes, signing and validation requirements for handling these signature purposes, and finally an architecture to be incorporated in workflow engines for handling these signature purposes.
The Item Perspective Classification was used to perform a 2-level classification of type of decision (rational/emotional) and content of items (psychological, social, biological, inorganic or pure experience).
The classification of the previous decision trees was compared with the original panel scores (Table 5).
This avoids the problem of not easily set threshold of classification decision function for the VCA method and enhances feasibility in real application.
