Multi-Label Classification Approach to Location Prediction (멀티 레이블 분류방법을 이용한 장소예측 연구)

Min Sung Lee (이민성)

doi:10.9708/jksci.2017.22.10.121

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).

KEYWORDS

Location Prediction, Multi-Label Classification, Data mining, Classifiers

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Multi-Label Classification Approach to Location Prediction

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Multi-Label Classification Approach to Location Prediction

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.