Research on Feature Selection in Near-Infrared Spectroscopy Classification Based on Improved Adaptive Genetic Algorithm Combined with ReliefF

2017 ◽  
Author(s):  
Ming Liu ◽  
Xiangqian Ding ◽  
Shusong Yu ◽  
Ruichun Hou
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Adaptive Genetic Algorithm

Feature Selection Algorithm Application in Near-Infrared Spectroscopy Classification Based on Binary Search Combined with Random Forest Pruning

2017 ◽  
Vol 54 (10) ◽  
pp. 103001
Author(s):  
刘 明 Liu Ming ◽  
李忠任 Li Zhongren ◽  
张海涛 Zhang Haitao ◽  
于春霞 Yu Chunxia ◽  
唐兴宏 Tang Xinghong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Infrared Spectroscopy ◽  
Random Forest ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Binary Search ◽  
Selection Algorithm ◽  
Feature Selection Algorithm

scholarly journals FEATURE SELECTION APPLIED TO THE TIME-FREQUENCY REPRESENTATION OF MUSCLE NEAR-INFRARED SPECTROSCOPY (NIRS) SIGNALS: CHARACTERIZATION OF DIABETIC OXYGENATION PATTERNS

2012 ◽  
Vol 12 (04) ◽  
pp. 1240013 ◽  
Author(s):  
SAMANTA ROSATI ◽  
GABRIELLA BALESTRA ◽  
FILIPPO MOLINARI
Keyword(s):  
Feature Selection ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Rough Set ◽  
Near Infrared ◽  
Attribute Reduction ◽  
Diabetic Patients ◽  
Time Frequency ◽  
Before And After ◽  
Concentration Changes

Diabetic patients might present peripheral microcirculation impairment and might benefit from physical training. Thirty-nine diabetic patients underwent the monitoring of the tibialis anterior muscle oxygenation during a series of voluntary ankle flexo-extensions by near-infrared spectroscopy (NIRS). NIRS signals were acquired before and after training protocols. Sixteen control subjects were tested with the same protocol. Time-frequency distributions of the Cohen's class were used to process the NIRS signals relative to the concentration changes of oxygenated and reduced hemoglobin. A total of 24 variables were measured for each subject and the most discriminative were selected by using four feature selection algorithms: QuickReduct, Genetic Rough-Set Attribute Reduction, Ant Rough-Set Attribute Reduction, and traditional ANOVA. Artificial neural networks were used to validate the discriminative power of the selected features. Results showed that different algorithms extracted different sets of variables, but all the combinations were discriminative. The best classification accuracy was about 70%. The oxygenation variables were selected when comparing controls to diabetic patients or diabetic patients before and after training. This preliminary study showed the importance of feature selection techniques in NIRS assessment of diabetic peripheral vascular impairment.

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