A Multiclass Robust Twin Parametric Margin Support Vector Machine with an Application to Vehicles Emissions

被引:1
作者
De Leone, Renato [1 ]
Maggioni, Francesca [2 ]
Spinelli, Andrea [2 ]
机构
[1] Univ Camerino, Sch Sci & Technol, Via Madonna Carceri 9, I-62032 Camerino, Italy
[2] Univ Bergamo, Dept Management Informat & Prod Engn, Viale G Marconi 5, I-24044 Dalmine, Italy
来源
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2023, PT II | 2024年 / 14506卷
关键词
Multiclass Classification; Support Vector Machine; Robust Optimization; REGRESSION;
D O I
10.1007/978-3-031-53966-4_22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper considers the problem of predicting vehicles smog rating by applying a novel Support Vector Machine (SVM) technique. Classical SVM-type models perform a binary classification of the training observations. However, in many real-world applications only two classifying categories may not be enough. For this reason, a new multiclass Twin Parametric Margin Support Vector Machine (TPMSVM) is designed. On the basis of different characteristics, such as engine size and fuel consumption, the model aims to assign each vehicle to a specific smog rating class. To protect the model against uncertainty arising in the measurement procedure, a robust optimization extension of the multiclass TPMSVM model is formulated. Spherical uncertainty sets are considered and a tractable robust counterpart of the model is derived. Experimental results on a real-world dataset show the good performance of the robust formulation.
引用
收藏
页码:299 / 310
页数:12
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