scholarly journals Configurations of Soft Decision-Making Methods Provided in fpfs-Matrices Space to Render Them Operable in ifpifs-Matrices Spaces and Their Application to Performance Ranking of the Noise Removal Filters

2021 ◽  
Author(s):  
Burak ARSLAN ◽  
Tuğçe AYDIN ◽  
Samet MEMİŞ ◽  
Serdar ENGİNOĞLU
Keyword(s):  
Decision Making ◽  
Noise Removal ◽  
Soft Decision ◽  
Performance Ranking

Application and comparative study of new optimization method for performance ranking of friction materials

2017 ◽  
Vol 232 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Vishal Mahale ◽  
Jayashree Bijwe ◽  
Sujeet Sinha
Keyword(s):  
Decision Making ◽  
Comparative Study ◽  
Decision Model ◽  
Evaluation Method ◽  
Multiple Criteria Decision Making ◽  
Optimization Method ◽  
Friction Material ◽  
Multiple Criteria ◽  
Friction Materials ◽  
Performance Ranking

Good friction materials should satisfy diverse and contradictory performance requirements such as adequate friction ( µ ≈ 0.35–0.45), resistance to wear, fade, squeal, judder, etc. in consort with good recovery and less noise producing tendency. To achieve center point of all these conflicting criteria and selection of best overall performing friction material is multiple criteria decision making (MCDM) problem and very difficult task. Decision maker can easily make decision with single criteria without the help of any optimization tool by maximizing beneficial criteria and minimizing non-beneficial criteria. However, it is extremely challenging task if decision making involves several number of conflicting criteria. Few techniques are reported in the literature such as ‘multiple criteria decision model’, ‘Multi-attribute decision model’, ‘extension evaluation method’ (EEM), etc. for performance ranking of friction materials. However, the simplicity, reliability, applicability, time devoted for the analysis, etc. are always most important aspects of selecting a right tool for the analysis. In this paper application of a technique ‘multiple objective optimization on the basis of ratio analysis’ (MOORA) has been first time employed for performance ranking of friction materials. A comparative study of MOORA and currently used methods MCDM and EEM are also presented. MOORA proved to be the best tool based on the criteria such as simple to use, fast, flexible, and efficient one.

scholarly journals Algorithms Based on COPRAS and Aggregation Operators with New Information Measures for Possibility Intuitionistic Fuzzy Soft Decision-Making

2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Harish Garg ◽  
Rishu Arora
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Score Function ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Soft Decision ◽  
Information Measures ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

The objective of this paper is to present novel algorithms for solving the multiple attribute decision-making problems under the possibility intuitionistic fuzzy soft set (PIFSS) information. The prominent characteristics of the PIFSS are that it considers the membership and nonmembership degrees of each object during evaluation and their corresponding possibility degree. Keeping these features, this paper presents some new operation laws, score function, and comparison laws between the pairs of the PIFSSs. Further, we define COmplex PRoportional ASsessment (COPRAS) and weighted averaging and geometric aggregation operators to aggregate the PIFSS information into a single one. Later, we develop two algorithms based on COPRAS and aggregation operators to solve decision-making problems. In these approaches, the experts and the weights of the parameters are determined with the help of entropy and the distance measure to remove the ambiguity in the information. Finally, a numerical example is given to demonstrate the presented approaches.

RED: a new method for performance ranking of large decision making units

2015 ◽  
Vol 21 (5) ◽  
pp. 1271-1290 ◽  
Author(s):  
Mohammadreza Farahmand ◽  
Mohammad Ishak Desa
Keyword(s):  
Decision Making ◽  
New Method ◽  
Decision Making Units ◽  
Performance Ranking

Interval-valued Fuzzy Soft Decision Making Methods Based on MABAC, Similarity Measure and EDAS

2017 ◽  
Vol 152 (4) ◽  
pp. 373-396 ◽  
Author(s):  
Xindong Peng ◽  
Jingguo Dai ◽  
Huiyong Yuan
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Soft Decision ◽  
Interval Valued

scholarly journals Combining MLC and SVM Classifiers for Learning Based Decision Making: Analysis and Evaluations

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Zhang ◽  
Jinchang Ren ◽  
Jianmin Jiang
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Dna Sequences ◽  
Support Vector ◽  
Nonparametric Method ◽  
Soft Decision ◽  
Vector Machines ◽  
Maximum Likelihood Classifier ◽  
Non Gaussian ◽  
Combined Classifier

Maximum likelihood classifier (MLC) and support vector machines (SVM) are two commonly used approaches in machine learning. MLC is based on Bayesian theory in estimating parameters of a probabilistic model, whilst SVM is an optimization based nonparametric method in this context. Recently, it is found that SVM in some cases is equivalent to MLC in probabilistically modeling the learning process. In this paper, MLC and SVM are combined in learning and classification, which helps to yield probabilistic output for SVM and facilitate soft decision making. In total four groups of data are used for evaluations, covering sonar, vehicle, breast cancer, and DNA sequences. The data samples are characterized in terms of Gaussian/non-Gaussian distributed and balanced/unbalanced samples which are then further used for performance assessment in comparing the SVM and the combined SVM-MLC classifier. Interesting results are reported to indicate how the combined classifier may work under various conditions.

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