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http://hdl.handle.net/1942/13314
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DC Field | Value | Language |
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dc.contributor.author | YANG, Banghua | - |
dc.contributor.author | JANSSENS, Davy | - |
dc.contributor.author | RUAN, Da | - |
dc.contributor.author | COOLS, Mario | - |
dc.contributor.author | BELLEMANS, Tom | - |
dc.contributor.author | WETS, Geert | - |
dc.date.accessioned | 2012-03-05T15:15:24Z | - |
dc.date.available | 2012-03-05T15:15:24Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Wang, Yinglin & Li, Tianrui (Ed.) Advances in Intelligent and Soft Computing, 122 (2012), p. 249-257 | - |
dc.identifier.isbn | 9783642256639 | - |
dc.identifier.issn | 1867-5662 | - |
dc.identifier.uri | http://hdl.handle.net/1942/13314 | - |
dc.description.abstract | 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. | - |
dc.language.iso | en | - |
dc.publisher | Springer-Verlag Berlin Heidelberg | - |
dc.relation.ispartofseries | Advances in Intelligent and Soft Computing | - |
dc.rights | © Springer-Verlag Berlin Heidelberg 2011. | - |
dc.subject.other | activity-based transportation models; support vector machine (SVM); data imputation; missing data | - |
dc.title | A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models | - |
dc.type | Proceedings Paper | - |
local.bibliographicCitation.authors | Wang, Yinglin | - |
local.bibliographicCitation.authors | Li, Tianrui | - |
local.bibliographicCitation.conferencedate | December 15-17, 2011 | - |
local.bibliographicCitation.conferencename | 6th International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2011) | - |
local.bibliographicCitation.conferenceplace | Shanghai, China | - |
dc.identifier.epage | 257 | - |
dc.identifier.issue | 2012 | - |
dc.identifier.spage | 249 | - |
dc.identifier.volume | 122 | - |
local.bibliographicCitation.jcat | C1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Proceedings Paper | - |
dc.bibliographicCitation.oldjcat | C1 | - |
dc.identifier.doi | 10.1007/978-3-642-25664-6_29 | - |
dc.identifier.isi | 000310937600033 | - |
dc.identifier.url | http://orbi.ulg.ac.be/handle/2268/134331 | - |
local.bibliographicCitation.btitle | Advances in Intelligent and Soft Computing | - |
item.contributor | YANG, Banghua | - |
item.contributor | JANSSENS, Davy | - |
item.contributor | RUAN, Da | - |
item.contributor | COOLS, Mario | - |
item.contributor | BELLEMANS, Tom | - |
item.contributor | WETS, Geert | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2013 | - |
item.fullcitation | YANG, Banghua; JANSSENS, Davy; RUAN, Da; COOLS, Mario; BELLEMANS, Tom & WETS, Geert (2012) A Data Imputation Method with Support Vector Machines for Activity-Based Transportation Models. In: Wang, Yinglin & Li, Tianrui (Ed.) Advances in Intelligent and Soft Computing, 122 (2012), p. 249-257. | - |
item.accessRights | Open Access | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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springerA Data Imputation Method.pdf | Peer-reviewed author version | 192.82 kB | Adobe PDF | View/Open |
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