A hybrid approach based on ELECTRE III ‐genetic algorithm and TOPSIS method for selection of optimal COVID ‐19 vaccines

2021 ◽  
Author(s):  
Roberto Louis Forestal ◽  
Shih‐Ming Pi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Approach ◽  
Topsis Method ◽  
Electre Iii ◽  
Selection Of

A Hybrid Approach for Feature Selection Based on Genetic Algorithm and Recursive Feature Elimination

2021 ◽  
Vol 12 (2) ◽  
pp. 17-38
Author(s):  
Pooja Rani ◽  
Rajneesh Kumar ◽  
Anurag Jain ◽  
Sunil Kumar Chawla
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Classification Systems ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Sensitivity Specificity ◽  
Selection Of

Machine learning has become an integral part of our life in today's world. Machine learning when applied to real-world applications suffers from the problem of high dimensional data. Data can have unnecessary and redundant features. These unnecessary features affect the performance of classification systems used in prediction. Selection of important features is the first step in developing any decision support system. In this paper, the authors have proposed a hybrid feature selection method GARFE by integrating GA (genetic algorithm) and RFE (recursive feature elimination) algorithms. Efficiency of proposed method is analyzed using support vector machine classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. Proposed GARFE method is also compared to eight other feature selection methods. Results demonstrate that the proposed GARFE method has increased the performance of classification systems by removing irrelevant and redundant features.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

scholarly journals Selection of Scholarship Recipient by Implementing Genetic Algorithm and Fuzzy Logic

2021 ◽  
Vol 1933 (1) ◽  
pp. 012069
Author(s):  
Yohanssen Pratama ◽  
Monalisa Pasaribu ◽  
Joni Nababan ◽  
Dayani Sihombing ◽  
Dicky Gultom
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Selection Of

Selection of Input Variables for the Prediction of Wind Speed in Wind Farms Based on Genetic Algorithm

2011 ◽  
Vol 35 (6) ◽  
pp. 649-660 ◽  
Author(s):  
R. A. Gupta ◽  
Rajesh Kumar ◽  
Ajay Kumar Bansal
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Farms ◽  
Input Variables ◽  
Selection Of

Research on Material Selection with Multi-Attribute Decision Method and G1 Method

2014 ◽  
Vol 952 ◽  
pp. 20-24 ◽  
Author(s):  
Xue Jun Xie
Keyword(s):  
Material Selection ◽  
Selection Process ◽  
Selection Problem ◽  
Topsis Method ◽  
Attribute Weight ◽  
Multi Attribute Decision Making ◽  
Judgment Matrix ◽  
Optimal Material ◽  
Selection Of ◽  
Better Than

The selection of an optimal material is an important aspect of design for mechanical, electrical, thermal, chemical or other application. Many factors (attributes) need to be considered in material selection process, and thus material selection problem is a multi-attribute decision making (MADM) problem. This paper proposes a new MADM method for material selection problem. G1 method does not need to test consistency of the judgment matrix. Thus it is better than AHP. In this paper, firstly, we use the G1 method to determine the attribute weight. Then TOPSIS method is used to calculate the closeness of the candidate materials with respect positive solution. A practical material selection case is used to demonstrate the effectiveness and feasibility of the proposed method.

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