An Optimized Multi-class Classification Algorithm Based on SVM Decision Tree

2010 ◽  
Author(s):  
Chen Donghui ◽  
Liu Zhijing
Keyword(s):  
Decision Tree ◽  
Classification Algorithm ◽  
Multi Class Classification

Machine Learning Classification of Head Impact Sensor Data

2019 ◽  
Author(s):  
Tyler F. Rooks ◽  
Andrea S. Dargie ◽  
Valeta Carol Chancey
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
External Validation ◽  
Classification Algorithm ◽  
Sensor Data ◽  
Environmental Sensors ◽  
Head Acceleration ◽  
Machine Learning Classification ◽  
Environmental Sensor ◽  
Validation Set

Abstract A shortcoming of using environmental sensors for the surveillance of potentially concussive events is substantial uncertainty regarding whether the event was caused by head acceleration (“head impacts”) or sensor motion (with no head acceleration). The goal of the present study is to develop a machine learning model to classify environmental sensor data obtained in the field and evaluate the performance of the model against the performance of the proprietary classification algorithm used by the environmental sensor. Data were collected from Soldiers attending sparring sessions conducted under a U.S. Army Combatives School course. Data from one sparring session were used to train a decision tree classification algorithm to identify good and bad signals. Data from the remaining sparring sessions were kept as an external validation set. The performance of the proprietary algorithm used by the sensor was also compared to the trained algorithm performance. The trained decision tree was able to correctly classify 95% of events for internal cross-validation and 88% of events for the external validation set. Comparatively, the proprietary algorithm was only able to correctly classify 61% of the events. In general, the trained algorithm was better able to predict when a signal was good or bad compared to the proprietary algorithm. The present study shows it is possible to train a decision tree algorithm using environmental sensor data collected in the field.

A gradient boosting decision tree based GPS signal reception classification algorithm

2020 ◽  
Vol 86 ◽  
pp. 105942 ◽  
Author(s):  
Rui Sun ◽  
Guanyu Wang ◽  
Wenyu Zhang ◽  
Li-Ta Hsu ◽  
Washington Y. Ochieng
Keyword(s):  
Decision Tree ◽  
Classification Algorithm ◽  
Gradient Boosting
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