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Multi-Label Classification Approach to Location Prediction

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2017, 22(10), pp.121-128
  • DOI : 10.9708/jksci.2017.22.10.121
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 27, 2017
  • Accepted : October 16, 2017
  • Published : October 31, 2017

Min Sung Lee 1

1이엠 어널리틱스

Accredited

ABSTRACT

In this paper, we propose a multi-label classification method in which multi-label classification estimation techniques are applied to resolving location prediction problem. Most of previous studies related to location prediction have focused on the use of single-label classification by using contextual information such as user’s movement paths, demographic information, etc. However, in this paper, we focused on the case where users are free to visit multiple locations, forcing decision-makers to use multi-labeled dataset. By using 2373 contextual dataset which was compiled from college students, we have obtained the best results with classifiers such as bagging, random subspace, and decision tree with the multi-label classification estimation methods like binary relevance(BR), binary pairwise classification (PW).

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