A Deep-Layer Feature Selection Method Based on Deep Neural Networks

2018 ◽  
pp. 542-551
Author(s):  
Chen Qiao ◽  
Ke-Feng Sun ◽  
Bin Li
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Deep Neural Networks ◽  
Deep Layer ◽  
Feature Selection Method ◽  
Selection Method

scholarly journals TEHRAN AIR POLLUTANTS PREDICTION BASED ON RANDOM FOREST FEATURE SELECTION METHOD

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 483-488 ◽  
Author(s):  
A. Shamsoddini ◽  
M. R. Aboodi ◽  
J. Karami
Keyword(s):  
Neural Networks ◽  
Air Pollution ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Random Forest ◽  
Sulfur Dioxide ◽  
Nitrogen Dioxide ◽  
Feature Selection Method ◽  
Selection Method ◽  
The Other

Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

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