scholarly journals Modern Metaheuristic with Multi-Objective Formulation for the Variable Selection Problem

2017 ◽  
Vol 13 (11) ◽  
pp. 659-666
Author(s):  
Lauro Cassio Martins de Paula ◽  
Anderson da Silva Soares ◽  
Telma Woerle Soares ◽  
Anselmo Elcana Pereira ◽  
Clarimar José Coelho
Keyword(s):  
Variable Selection ◽  
Selection Problem ◽  
Multi Objective ◽  
Variable Selection Problem

The Variable Selection Problem in the Three Worlds of Welfare Literature

2019 ◽  
Vol 144 (2) ◽  
pp. 625-646 ◽  
Author(s):  
Erdem Yörük ◽  
İbrahim Öker ◽  
Kerem Yıldırım ◽  
Burcu Yakut-Çakar
Keyword(s):  
Variable Selection ◽  
Selection Problem ◽  
Variable Selection Problem

Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

2014 ◽  
Vol 4 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Lauro C. M. de Paula ◽  
Anderson S. Soares ◽  
Telma W. L. Soares ◽  
Alexandre C. B. Delbem ◽  
Clarimar J. Coelho ◽  
...  
Keyword(s):  
Variable Selection ◽  
Graphics Processing Units ◽  
Firefly Algorithm ◽  
Multivariate Calibration ◽  
Selection Problem ◽  
Calibration Problem ◽  
New Strategy ◽  
Graphics Processing ◽  
Processing Platform ◽  
Variable Selection Problem

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable selection problem in multivariate calibration. The main drawback of this task lies in its computation burden, as it grows polynomially with the number of variables available. In this context, this paper proposes a GPU-based FA for variable selection in a multivariate calibration problem. Such implementation is aimed at improving the computational efficiency of the algorithm. For this purpose, a new strategy of regression coefficients calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving a large number of variables. In such example, gains of speedup were obtained. Additionally the authors also demonstrate that the FA, in comparison with traditional algorithms, can be a relevant contribution for the variable selection problem.

scholarly journals Nonparametric Methods and Evolutionary Algorithms in Genetic Epidemiology

2018 ◽  
Author(s):  
Colleen Molloy Farrelly
Keyword(s):  
Random Forest ◽  
Evolutionary Algorithms ◽  
Variable Selection ◽  
Genetic Epidemiology ◽  
Regression Models ◽  
Selection Problem ◽  
High Dimensionality ◽  
Two Stage ◽  
Selection Methodology ◽  
Variable Selection Problem

Paper overviews variable selection problem in high dimensionality, particularly focused on genetic psychiatry and genetic epidemiology in general. Genetic and quantum evolutionary algorithms, tree-based classification/regression models, random forest, and other approaches are detailed. Paper concludes with a roadmap for new algorithm and two-stage selection methodology.

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