Appendix S: Tables for Multivariate Selection Problems

1999 ◽  
pp. 516-530
Keyword(s):  
Selection Problems

Decision Aids for Selection Problems

1997 ◽  
Vol 48 (5) ◽  
pp. 541-541
Author(s):  
D L Olson
Keyword(s):  
Decision Aids ◽  
Selection Problems

Multi-Goal Subset Selection Problems under Uncertainty

1979 ◽  
Vol 11 (1) ◽  
pp. 61-69 ◽  
Author(s):  
Pat R. Odom ◽  
Robert E. Shannon ◽  
Billy P. Buckles
Keyword(s):  
Subset Selection ◽  
Selection Problems

scholarly journals An improved multi-choice goal programming approach for supplier selection problems

2015 ◽  
Vol 39 (14) ◽  
pp. 4213-4222 ◽  
Author(s):  
O. Jadidi ◽  
S. Cavalieri ◽  
S. Zolfaghari
Keyword(s):  
Goal Programming ◽  
Supplier Selection ◽  
Programming Approach ◽  
Selection Problems

Relationship between Naïve Bayes error and max-dependency criterion in feature selection problems

ICCKE 2013 ◽  
2013 ◽  
Author(s):  
Nafiseh Sedaghat ◽  
Mahmood Fathy ◽  
Mohammad-Hossein Modarressi
Keyword(s):  
Feature Selection ◽  
Ve Bayes ◽  
Selection Problems ◽  
Bayes Error

Catalog Design: Design Using Available Assets

1992 ◽  
Author(s):  
S. Vadde ◽  
J. K. Allen ◽  
F. Mistree
Keyword(s):  
Deterministic Model ◽  
Limited Resources ◽  
Complex Task ◽  
Cooling Fluid ◽  
Selection Problems ◽  
Catalog Design ◽  
Standard Framework ◽  
Selection Of ◽  
Do So

Abstract Catalog design is a procedure in which a system is assembled by selecting standard components from catalogs of available components. Selection in design involves making a choice among a number of alternatives taking into account several attributes. The information available to a designer to do so during the early stages of project initiation may be uncertain. The uncertainty in information may be imprecise or stochastic. Under these circumstances, a designer has to balance limited resources against the quality of solution obtained or decisions made by accounting for uncertainty in information available. This complex task becomes formidable when dealing with coupled selection problems, that is problems that should be solved simultaneously. Coupled selection problems share a number of coupling attributes among them. In an earlier paper we have shown how selection problems, both coupled and uncoupled can be reformulated as a single compromise Decision Support Problem (DSP) using a deterministic model. In this paper, we show how the traditional compromise DSP can be extended to represent a nondeterministic case. We use fuzzy set theory to model imprecision and Bayesian statistics to model stochastic information. Formulations that can be solved with the same solution scheme are presented to handle both fuzzy and stochastic information in the standard framework of a compromise DSP. The approaches are illustrated by an example involving the coupled selection of a heat exchanger concept and a cooling fluid for a specific application. The emphasis in this paper is placed on explaining the methods.

scholarly journals Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

2018 ◽  
Vol 145 ◽  
pp. 25-45 ◽  
Author(s):  
Majdi Mafarja ◽  
Ibrahim Aljarah ◽  
Ali Asghar Heidari ◽  
Abdelaziz I. Hammouri ◽  
Hossam Faris ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Feature Selection ◽  
Selection Problems ◽  
Grasshopper Optimization
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