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A HMM-Based Location Prediction Framework with Location Recognizer Combining k-Nearest Neighbor and Multiple Decision Trees

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Hybrid Artificial Intelligent Systems (HAIS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8073))

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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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Author information

Authors and Affiliations

  1. Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-749, Korea

    Yong-Joong Kim & Sung-Bae Cho

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  1. Yong-Joong Kim
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  2. Sung-Bae Cho
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Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios M. Polycarpou

  3. Department of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  4. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  5. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Héctor Quintián  & Emilio Corchado  & 

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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: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_62

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  • DOI: https://doi.org/10.1007/978-3-642-40846-5_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40845-8

  • Online ISBN: 978-3-642-40846-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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