Alternative approach for learning and improving the MCDA method PROAFTN

Abstract

OBJECTIVES. The objectives of this paper are 1) to propose new techniques to learn and improve the multi-criteria 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. METH-ODS. 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. RESULTS. The comparative study is based on the generated classification accuracy of the algorithms on the datasets. For further robust analysis of the experiments, we used the Friedman statistical measure with the corresponding posthoc tests. CONCLUSION. The proposed approaches significantly improved the performance of the classification method PROAFTN. Based on the generated results on the same datasets, PROAFTN 1 outperforms widely used classification algorithms. Furthermore, the method is simple, no preprocessing is required, and no loss of information during learning.

Combining Machine Learning and Metaheuristics Algorithms for Classification Method PROAFTN

“…(13) is used. As a result, the parameters d 1 jh and d 2 jh are used instead of q 1 jh and q 2 jh , respectively. Therefore, the optimization problem, which is based on maximizing classification accuracy providing the optimal parameters S 1 jh , S 2 jh , d 1 jh , d 2 jh and w jh , is defined here,…”

“…The field of MCDA [10,63] includes a wide variety of tools and methodologies developed for the purpose of helping a decision model (DM) to select from finite sets of alternatives according to two or more criteria [62]. In MCDA, the classification problems can be distinguished from other classification problems within the machine learning framework from two perspectives [2]. The first includes the characteristics describing the objects, which are assumed to have the form of decision criteria, providing not only a description of the objects but also some additional preferential information associated with each attribute [22,51].…”

Multicriteria decision analysis for sustainable data centers location

“…There are several MCDA sorting methods, most of them surveyed by Doumpos and Zopounidis () and Zopounidis and Doumpos (). For later examples we can cite, inter alia, Al‐Obeidat and Belacel (), Fernandez et al. (), Figueira et al.…”