Feature selection for Human resource selection based on Affinity Propagation and SVM sensitivity analysis

2009 ◽  
Author(s):  
Qiangwei Wang ◽  
Boyang Li ◽  
Jinglu Hu
Keyword(s):  
Sensitivity Analysis ◽  
Feature Selection ◽  
Human Resource ◽  
Resource Selection ◽  
Affinity Propagation ◽  
Selection For

scholarly journals Dynamic human resource selection for business process exceptions

2018 ◽  
Vol 26 (1) ◽  
pp. 23-31 ◽  
Author(s):  
Jooseok Lee ◽  
Seunghoon Lee ◽  
Jinwoo Kim ◽  
Injun Choi
Keyword(s):  
Human Resource ◽  
Business Process ◽  
Resource Selection ◽  
Selection For

Human Resource Selection for Manufacturing System Using Petri Nets

2015 ◽  
Vol 791 ◽  
pp. 132-140 ◽  
Author(s):  
Grzegorz Kłosowski ◽  
Arkadiusz Gola ◽  
Antoni Świć
Keyword(s):  
Computer Simulation ◽  
Petri Nets ◽  
Human Resource ◽  
Resource Selection ◽  
Manufacturing System ◽  
Computer Modelling ◽  
Objective Functions ◽  
Proper Selection ◽  
Selection For ◽  
Selection Of

Proper selection of personnel constitutes a frequent challenge for the management of many enterprises. In this paper the above problem has been defined using three objective functions which required simultaneous optimisation. To solve this problem, computer modelling based on Petri nets was proposed. The model was subjected to iterative computer simulation, during which various variants of workstation assignment were tested. This resulted in the emergence of a variant which best fulfilled the assumed optimisation criteria.

A Methodology for Feature Selection Using Multiobjective Genetic Algorithms for Handwritten Digit String Recognition

2003 ◽  
Vol 17 (06) ◽  
pp. 903-929 ◽  
Author(s):  
L. S. Oliveira ◽  
R. Sabourin ◽  
F. Bortolozzi ◽  
C. Y. Suen
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Feature Selection ◽  
Multiobjective Genetic Algorithm ◽  
Pattern Recognition Problem ◽  
Multiobjective Genetic Algorithms ◽  
Handwritten Digit ◽  
Digit String ◽  
Selection For

In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multiobjective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Some advantages of this approach include the ability to accommodate multiple criteria such as number of features and accuracy of the classifier, as well as the capacity to deal with huge databases in order to adequately represent the pattern recognition problem. Comprehensive experiments on the NIST SD19 demonstrate the feasibility of the proposed methodology.

Chicken Algorithm Based Feature Selection For Arabic Information Searching System

2019 ◽  
pp. 389
Author(s):  
زينب عبدالأمير ◽  
علياء كريم عبدالحسن
Keyword(s):  
Feature Selection ◽  
Information Searching ◽  
Selection For
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