A two-level approach to choose the cost parameter in support vector machines☆

2008 ◽  
Vol 34 (2) ◽  
pp. 1366-1370 ◽  
Author(s):  
Y DONG ◽  
Z XIA ◽  
Z XIA
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Cost Parameter ◽  
Vector Machines ◽  
The Cost

scholarly journals HEARTBEAT CLASSIFICATION USING SUPPORT VECTOR MACHINES (SVMs) WITH AN EMBEDDED REJECT OPTION

2012 ◽  
Vol 26 (01) ◽  
pp. 1250001 ◽  
Author(s):  
ZAHIA ZIDELMAL ◽  
AHMED AMIROU ◽  
ADEL BELOUCHRANI
Keyword(s):  
Support Vector Machines ◽  
Performance Enhancement ◽  
Support Vector ◽  
Heartbeat Classification ◽  
Medical Diagnoses ◽  
Hinge Loss ◽  
Beat Classification ◽  
Vector Machines ◽  
Significant Performance ◽  
The Cost

In this paper, we introduce a new system for ECG beat classification using support vector machines classifier with a double hinge loss. The proposed classifier rejects samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the consequence of a wrong classification can be so harmful that it is convenient to reject such sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds according to the cost of rejecting or misclassifying a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrythmia database. The achieved results are represented by the error reject tradeoff. We obtained 98.2% of sensitivity with no rejection and more than 99% of sensitivity for the optimal classification cost being competitive to other published studies.

scholarly journals Development of Cost Estimation Models Based on ANN Ensembles and the SVM Method

2020 ◽  
Vol 30 (3) ◽  
pp. 48-67
Author(s):  
Michał Juszczyk
Keyword(s):  
Support Vector Machines ◽  
Cost Estimation ◽  
Construction Projects ◽  
Predictive Performance ◽  
Design Stage ◽  
Support Vector ◽  
Early Design Stage ◽  
Vector Machines ◽  
Structural Frames ◽  
The Cost

Abstract Cost estimation, as one of the key processes in construction projects, provides the basis for a number of project-related decisions. This paper presents some results of studies on the application of artificial intelligence and machine learning in cost estimation. The research developed three original models based either on ensembles of neural networks or on support vector machines for the cost prediction of the floor structural frames of buildings. According to the criteria of general metrics (RMSE, MAPE), the three models demonstrate similar predictive performance. MAPE values computed for the training and testing of the three developed models range between 5% and 6%. The accuracy of cost predictions given by the three developed models is acceptable for the cost estimates of the floor structural frames of buildings in the early design stage of the construction project. Analysis of error distribution revealed a degree of superiority for the model based on support vector machines.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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