A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees

Yong Joong Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Knowing user's current or next location is very important task for context-aware services in mobile environment. Many researchers have tried to predict user location using their own methods. However, they focused mainly the performance of method, and only few were considered development of real working system on mobile devices. In this paper, we present a location prediction framework, and develop a personalized destination prediction system based on this framework using smartphone. The framework consists of two methods of recognizing user location based on the combined method of k-nearest neighbor (kNN) and decision tree, and predicting user destination based on the hidden Markov model (HMM). The destination prediction system is composed of four parts including mobile sensor log collector, location recognition module, location prediction module, and system management module. Experiments on real datasets of five persons showed that our method achieved average prediction accuracy above 87%.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings
Pages618-628
Number of pages11
DOIs
Publication statusPublished - 2013 Oct 8
Event8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013 - Salamanca, Spain
Duration: 2013 Sep 112013 Sep 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8073 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013
CountrySpain
CitySalamanca
Period13/9/1113/9/13

Fingerprint

Hidden Markov models
Decision trees
Decision tree
Markov Model
Nearest Neighbor
Model-based
Prediction
Module
Smartphones
Combined Method
Context-aware
Mobile devices
Mobile Devices
Framework
Person
Predict
Sensor
Sensors
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, Y. J., & Cho, S. B. (2013). A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. In Hybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings (pp. 618-628). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8073 LNAI). https://doi.org/10.1007/978-3-642-40846-5_62
Kim, Yong Joong ; Cho, Sung Bae. / A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. Hybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings. 2013. pp. 618-628 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Knowing user's current or next location is very important task for context-aware services in mobile environment. Many researchers have tried to predict user location using their own methods. However, they focused mainly the performance of method, and only few were considered development of real working system on mobile devices. In this paper, we present a location prediction framework, and develop a personalized destination prediction system based on this framework using smartphone. The framework consists of two methods of recognizing user location based on the combined method of k-nearest neighbor (kNN) and decision tree, and predicting user destination based on the hidden Markov model (HMM). The destination prediction system is composed of four parts including mobile sensor log collector, location recognition module, location prediction module, and system management module. Experiments on real datasets of five persons showed that our method achieved average prediction accuracy above 87{\%}.",
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Kim, YJ & Cho, SB 2013, A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. in Hybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8073 LNAI, pp. 618-628, 8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013, Salamanca, Spain, 13/9/11. https://doi.org/10.1007/978-3-642-40846-5_62

A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. / Kim, Yong Joong; Cho, Sung Bae.

Hybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings. 2013. p. 618-628 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8073 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kim YJ, Cho SB. A HMM-based location prediction framework with location recognizer combining k-nearest neighbor and multiple decision trees. In Hybrid Artificial Intelligent Systems - 8th International Conference, HAIS 2013, Proceedings. 2013. p. 618-628. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-40846-5_62