dc.contributor.advisor | | |
dc.contributor.author | Aggarwal, Manish | |
dc.date.accessioned | 2020-06-01T03:58:08Z | |
dc.date.available | 2020-06-01T03:58:08Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Aggarwal, M. (2019). Preferences-based learning of multinomial logit model. Knowledge and Information Systyems, 59(3), 523-538. doi:https://doi.org/10.1007/s10115-018-1215-9 | |
dc.identifier.issn | 0219-3116 | |
dc.identifier.uri | http://hdl.handle.net/11718/23039 | |
dc.description.abstract | We learn the parameters of the popular multinomial logit model to gain insights about a DM’s decision process. We accomplish this objective through the recent algorithmic advances in the emerging field of preference learning. The empirical evaluation of the proposed approach is performed on a set of 12 publicly available benchmark datasets. First experimental results suggest that our approach is not only intuitively appealing, but also competitive to state-of-the-art preference learning methods in terms of the prediction accuracy. | en_US |
dc.publisher | Springer | en_US |
dc.subject | Preference learning
Decision behavior
Choice modeling
Multi-attribute decision making | en_US |
dc.title | Preferences-based learning of multinomial logit model | en_US |
dc.type | Article | en_US |