Projects per year
In this paper we describe a method to reuse models with Model-Based Subgroup Discovery (MBSD), which is a extension of the Subgroup Discovery scheme. The task is to predict the number of bikes at a new rental station 3 hours in advance. Instead of training new models with the limited data from these new stations, our approach first selects a number of pre-trained models from old rental stations according to their mean absolute errors (MAE). For each selected model, we further performed MBSD to locate a number of subgroups that the selected model has a deviated prediction performance. Then another set of pre-trained models are selected only according to their MAE over the subgroup. Finally, the prediction are made by averaging the prediction from the models selected during the previous two steps. The experiments show that our method performances better than selecting trained models with the lowest MAE, and the averaged low-MAE models.
|Title of host publication||Proceedings of the ECML/PKDD 2015 Discovery Challenges|
|Subtitle of host publication||co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015)|
|Number of pages||14|
|Publication status||Published - 7 Dec 2015|
|Event||ECML/PKDD 2015 Discovery Challenges, ECML-PKDD-DCs 2015 - Porto, Portugal|
Duration: 7 Sep 2015 → 11 Sep 2015
|Name||CEUR Workshop Proceedings|
|Conference||ECML/PKDD 2015 Discovery Challenges, ECML-PKDD-DCs 2015|
|Period||7/09/15 → 11/09/15|
- Jean Golding
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent
1/02/13 → 1/08/16