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Alternative approach for learning and improving the MCDA method PROAFTN
Author(s) -
AlObeidat Feras,
Belacel Nabil
Publication year - 2011
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20476
Subject(s) - computer science , machine learning , artificial intelligence , preprocessor , data pre processing , multiple criteria decision analysis , data mining , construct (python library) , measure (data warehouse) , mathematics , mathematical optimization , programming language
The objectives of this paper are (1) to propose new techniques to learn and improve the multicriteria decision analysis (MCDA) method PROAFTN based on machine learning approaches and (2) to compare the performance of the developed methods with other well‐known machine learning classification algorithms. The proposed learning methods consist of two stages: The first stage involves using the discretization techniques to obtain the required parameters for the PROAFTN method, and the second stage is the development of a new inductive approach to construct PROAFTN prototypes for classification. The comparative study is based on the generated classification accuracy of the algorithms on the data sets. For further robust analysis of the experiments, we used the Friedman statistical measure with the corresponding post hoc tests. The proposed approaches significantly improved the performance of the classification method PROAFTN. Based on the generated results on the same data sets, PROAFTN outperforms widely used classification algorithms. Furthermore, the method is simple, no preprocessing is required, and no loss of information during learning. © 2011 Wiley Periodicals, Inc.