Projets par an
Résumé
This work contributes to develop a new methodology to identify empirical data-driven causal structure of a domain knowledge. We propose an algorithm as a two-stage procedure by first drawing
relevant prior partial relationships between variables and using them as structure constraints in a structure learning task of Bayesian Networks. The latter is then based on a model averaging approach
to obtain a statistically sound Bayesian network. The empirical study focuses on modeling commuters' travel mode choice. We present experimental results on testing the design of prior
restrictions, the effect of resampling size and learning algorithms, and the effect of random draw on fitted BN structure. The results show that the proposed method can capture more sophisticated
relationships between the variables that are missing in both decision tree models and random utility models.
langue originale | Anglais |
---|---|
Pages (de - à) | 299-325 |
journal | Transportmetrica A: Transport Science |
Volume | 13 |
Numéro de publication | 4 |
Date de mise en ligne précoce | 25 nov. 2016 |
Les DOIs | |
état | Publié - 2017 |
Projets
- 1 Terminé
-
ACROSS: Assessing the Socio-Cultural Effects on Mobility Behaviours in Cross-Border Areas
Ma, T.-Y. (PI), Darud, B. (CoI), Gerber, P. (CoI), Klein, S. (CoI), Lannoy, P. (CoI) & Ramadier, T. (CoI)
Fonds National de la Recherche
1/02/11 → 30/11/14
Projet: Recherche