C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining with decision trees and decision rules
Future Generation Computer Systems - Special double issue on data mining
Data Mining
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Differential Evolution for learning the classification method PROAFTN
Knowledge-Based Systems
Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Multicriteria fuzzy assignment method: a useful tool to assist medical diagnosis
Artificial Intelligence in Medicine
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PROAFTN belongs to Multiple-Criteria Decision Aid (MCDA) paradigm and requires a several set of parameters for the purpose of classification. This study proposes a new inductive approach for obtaining these parameters from data. To evaluate the performance of developed learning approach, a comparative study between PROAFTN and a decision tree in terms of their learning methodology, classification accuracy, and interpretability is investigated in this paper. The major distinguished property of Decision tree is that its ability to generate classification models that can be easily explained. The PROAFTN method has also this capability, therefore avoiding a black box situation. Furthermore, according to the proposed learning approach in this study, the experimental results show that PROAFTN strongly competes with ID3 and C4.5 in terms of classification accuracy.