Passenger Classification for an Airport Movement Forecast System
With new services provided by airlines and travel agencies passengers gained more flexibility, but also their visibility for airports and airlines was decreased. We describe the profiling component of a movement forecast system to increase passenger transparency. Therefore we present three approache...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IAENG Society of Industrial Engineering
2009
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/48212 |
| _version_ | 1848758047519801344 |
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| author | Richter, S. Ortmann, C. Reiners, Torsten |
| author2 | Prof Craig Douglas |
| author_facet | Prof Craig Douglas Richter, S. Ortmann, C. Reiners, Torsten |
| author_sort | Richter, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | With new services provided by airlines and travel agencies passengers gained more flexibility, but also their visibility for airports and airlines was decreased. We describe the profiling component of a movement forecast system to increase passenger transparency. Therefore we present three approaches to learning classifiers and how they can be used for passenger classification. It is shown how the choice of attributes considered in classification influences classifier performance. This is used as a comparison criterion for learning algorithms. |
| first_indexed | 2025-11-14T09:37:47Z |
| format | Conference Paper |
| id | curtin-20.500.11937-48212 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:37:47Z |
| publishDate | 2009 |
| publisher | IAENG Society of Industrial Engineering |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-482122017-10-02T02:27:10Z Passenger Classification for an Airport Movement Forecast System Richter, S. Ortmann, C. Reiners, Torsten Prof Craig Douglas passenger classification comparing classifiers feature subset selection accuracy estimation With new services provided by airlines and travel agencies passengers gained more flexibility, but also their visibility for airports and airlines was decreased. We describe the profiling component of a movement forecast system to increase passenger transparency. Therefore we present three approaches to learning classifiers and how they can be used for passenger classification. It is shown how the choice of attributes considered in classification influences classifier performance. This is used as a comparison criterion for learning algorithms. 2009 Conference Paper http://hdl.handle.net/20.500.11937/48212 IAENG Society of Industrial Engineering restricted |
| spellingShingle | passenger classification comparing classifiers feature subset selection accuracy estimation Richter, S. Ortmann, C. Reiners, Torsten Passenger Classification for an Airport Movement Forecast System |
| title | Passenger Classification for an Airport Movement Forecast System |
| title_full | Passenger Classification for an Airport Movement Forecast System |
| title_fullStr | Passenger Classification for an Airport Movement Forecast System |
| title_full_unstemmed | Passenger Classification for an Airport Movement Forecast System |
| title_short | Passenger Classification for an Airport Movement Forecast System |
| title_sort | passenger classification for an airport movement forecast system |
| topic | passenger classification comparing classifiers feature subset selection accuracy estimation |
| url | http://hdl.handle.net/20.500.11937/48212 |