Model Reuse with Subgroup Discovery

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Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the ECML/PKDD 2015 Discovery Challenges
Subtitle of host publicationco-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015)
PublisherCEUR-WS
Number of pages14
Publication statusPublished - 7 Dec 2015
EventECML/PKDD 2015 Discovery Challenges, ECML-PKDD-DCs 2015 - Porto, Portugal
Duration: 7 Sep 201511 Sep 2015

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume1526
ISSN (Print)1613-0073

Conference

ConferenceECML/PKDD 2015 Discovery Challenges, ECML-PKDD-DCs 2015
CountryPortugal
CityPorto
Period7/09/1511/09/15

Structured keywords

  • Jean Golding
  • SPHERE

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