Reference : A Data Imputation Method with Support Vector Machines for Activity-Based Transportation ...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Civil engineering
Business & economic sciences : Special economic topics (health, labor, transportation…)
http://hdl.handle.net/2268/134331
A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models
English
Yang, Banghua [Universiteit Hasselt - UH > > > >]
Janssens, Davy [Universiteit Hasselt - UH > > > >]
Ruan, Da [Universiteit Hasselt - UH > > > >]
Cools, Mario mailto [Universiteit Hasselt - UH > > > >]
Bellemans, Tom [Universiteit Hasselt - UH > > > >]
Wets, Geert [Universiteit Hasselt - UH > > > >]
2011
Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011)
Wang, Y.
Li, T.
Springer-Verlag
Advances in Intelligent and Soft Computing; 122
249-258
Yes
No
International
978-3-642-25663-9
Berlin
Germany
Sixth International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
15-12-2011 to 17-12-2011
Shanghai
China
[en] Activity-based transportation models ; Support Vector Machine (SVM) ; Data imputation ; Missing data
[en] In this paper, a data imputation method with a Support Vector Machine (SVM) is proposed to solve the issue of missing data in activity-based diaries. Here two SVM models are established to predict the missing elements of ‘number of cars’ and ‘driver license’. The inputs of the former SVM model include five variables (Household composition, household income, Age oldest household member, Children age class and Number of household members). The inputs of the latter SVM model include three variables (personal age, work status and gender). The SVM models to predict the ‘number of cars’ and ‘driver license’ can achieve accuracies of 69% and 83% respectively. The initial experimental results show that missing elements of observed activity diaries can be accurately inferred by relating different pieces of information. Therefore, the proposed SVM data imputation method serves as an effective data imputation method in the case of missing information.
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/134331
10.1007/978-3-642-25664-6_29

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