Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation

PAMM ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 815-816
Author(s):  
James Kuria Kimotho ◽  
Walter Sextro
Keyword(s):  
Support Vector Machines ◽  
State Estimation ◽  
Optimal Parameter ◽  
Parameter Tuning ◽  
Support Vector ◽  
Health State ◽  
Vector Machines ◽  
Multiclass Support Vector Machines

scholarly journals Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

2016 ◽  
Vol 24 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Claudia Ehrentraut ◽  
Markus Ekholm ◽  
Hideyuki Tanushi ◽  
Jörg Tiedemann ◽  
Hercules Dalianis
Keyword(s):  
Support Vector Machines ◽  
Significant Risk ◽  
Parameter Tuning ◽  
Hospital Staff ◽  
Support Vector ◽  
Patient Records ◽  
Hospital Acquired Infections ◽  
Vector Machines ◽  
Patient Health ◽  
Hospital Acquired

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

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