Abstract
Long-term travel time predictions are crucial for tactical and operational public transport planning in schedule design and resource allocation tasks. Similarly to any regression task, its success considerably depend on an adequate feature selection framework. In this paper, we approach the myopia of the State-of-the-Art method RReliefF on mining relevant inter-relationships of the feature space relevant for reducing the entropy around the target variable on regression tasks. A comparative study was conducted using baseline regression methods and LASSO as a valid alternative to RReliefF. Experimental results obtained on a real-world case study uncovered the bias/variance reduction obtained by each approach, pointing out promising ideas on this research line.
Original language | English |
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Title of host publication | International Symposium on Intelligent Data Analysis |
Subtitle of host publication | Advances in Intelligent Data Analysis XV |
Editors | H. Boström, A. Knobbe, C. Soares, P. Papapetrou |
Publisher | Springer |
Pages | 98-109 |
Number of pages | 12 |
ISBN (Electronic) | 978-3-319-46349-0 |
ISBN (Print) | 978-3-319-46348-3 |
DOIs | |
Publication status | Published - 2016 |
Event | The 15th International Symposium on Intelligent Data Analysis - Stockholm, Sweden Duration: 13 Oct 2016 → 15 Oct 2016 Conference number: 15 http://ida2016.blogs.dsv.su.se/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 9897 |
Conference
Conference | The 15th International Symposium on Intelligent Data Analysis |
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Abbreviated title | IDA 2016 |
Country/Territory | Sweden |
City | Stockholm |
Period | 13/10/16 → 15/10/16 |
Internet address |
Keywords
- Travel time prediction
- Machine learning
- Regression
- Feature selection