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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8073 LNAI, Issue , 2013, Pages 618-628

  Kim, Yong Joong ^a   Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

Location extraction; Location prediction; Location recognition; Location based services

Indexed keywords

CONTEXT AWARE SERVICES; K NEAREST NEIGHBOR (KNN); K-NEAREST NEIGHBORS; LOCATION PREDICTION; LOCATION RECOGNITION; MOBILE ENVIRONMENTS; PREDICTION ACCURACY; PREDICTION SYSTEMS;

DECISION TREES; HIDDEN MARKOV MODELS; INFORMATION SERVICES; INTELLIGENT SYSTEMS; LOCATION BASED SERVICES; MOBILE DEVICES;

FORECASTING;

EID: 84884965010     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40846-5_62     Document Type: Conference Paper

References (24)

1. Alvarez-Garcia, J.A., Ortega, J.A., Gonzalez- Abril, L., Velasco, F.: Trip Destination Prediction Based on Past GPS Log Using a Hidden Markov Model. Expert Systems with Applications 37(12), 8166-8171 (2010)
2. Ashbrook, D., Starner, T.: Using GPS to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275-286 (2003)
3. Calabrese, F., Lorenzo, G.D., Ratti, C.: Human Mobility Prediction based on Individual and Collective Geographical Preferences. In: Proceedings of the 13th IEEE Intelligent Transportation Systems, pp. 312-317 (2010)
4. Caruana, R., Niculescu-Mizil, A.: An Empirical Comparison of Supervised Learning Algorithms. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 161-168 (2006)
5. Chen, G., Kotz, D.: A Survey of Context-Aware Computing Research. Technical Report TR2000-381, Dartmouth (November 2000)
6. Hamerly, G., Elkan, C.: Learning the k in k- means. In: Advanced in Neural Information Processing Systems, vol. 16 (2003)
7. Hightower, J., Consolvo, S., LaMarca, A., Smith, I., Hughes, J.: Learning and Recognizing the Places We Go. In: Beigl, M., Intille, S.S., Rekimoto, J., Tokuda, H. (eds.) UbiComp 2005. LNCS, vol. 3660, pp. 159-176. Springer, Heidelberg (2005)
8. Kang, J.H., Welbourne, W., Stewart, B., Borriello, G.: Extracting Places from Traces of Locations. In: Proceedings of the 2nd International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, pp. 110-118 (2005)
9. Krumm, J., Horvitz, E.: Predestination: Inferring destinations from partial trajectories. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 243-260. Springer, Heidelberg (2006) (Pubitemid 44618976)
10. Lee, Y.S., Cho, S.B.: An Efficient Energy Management System for Android Phone Using Bayesian Networks. In: Proceedings of the 32nd International Conference on Distributed Computing Systems Workshops, pp. 102-107 (2012)
11. Lim, T.S., Loh, W.Y., Shih, Y.S.: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 40(3), 203-228 (2000)
12. Löwe, R., Mandl, P., Weber, M.: Context Directory: A Context-Aware Service for Mobile Context-Aware Computing Applications by the Example of Google Android. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops 2012, pp. 76-81 (2012)
13. Mathew, W., Raposo, R., Martins, B.: Predicting future locations with hidden Markov models. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 911-918 (2012)
14. Monreale, A., Pinelli, F., Trasarti, R.: WhereNext: a Location Predictor on Trajectory Pattern Mining. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, pp. 637-646 (2009)
15. Morzy, M.: Mining Frequent Trajectories of Moving Objects for Location Prediction. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 667-680. Springer, Heidelberg (2007)
16. Noh, H.Y., Lee, J.H., Oh, S.W., Hwang, K.S., Cho, S.B.: Exploiting Indoor Location and Mobile Information for Context-Awareness Service. Information Processing and Management 48 (1), 1-12 (2012)
17. Park, H.S., Oh, K., Cho, S.B.: Bayesian Network-Based High-Level Context Recognition for Mobile Context Sharing in Cyber-Physical System. International Journal of Distributed Sensor Networks (2011)
18. Petzold, J., Pietzowski, A., Bagci, F., Trumler, W., Ungerer, T.: Prediction of Indoor Movements Using Bayesian Networks. In: Strang, T., Linnhoff-Popien, C. (eds.) LoCA 2005. LNCS, vol. 3479, pp. 211-222. Springer, Heidelberg (2005) (Pubitemid 41313087)
19. Petzold, J., Bagci, F., Trumler, W., Ungerer, T.: Comparison of Different Methods for Next Location Prediction. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 909-918. Springer, Heidelberg (2006) (Pubitemid 44568464)
20. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, Inc., San Francisco (1993)
21. Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257-286 (1989)
22. Scellato, S., Musolesi, M., Mascolo, C., Latora, V., Campbell, A.T.: NextPlace: A spatiotemporal prediction framework for pervasive systems. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 152-169. Springer, Heidelberg (2011)
23. Simmons, R., Browning, B., Zhang, Y., Sadekar, V.: Learning to Predict Driver Route and Destination Intent. In: Proceedings of the IEEE Intelligent Transportation Systems Conference 2006, pp. 127-132 (2006)
24. Yavas, G., Katsaros, D., Ulusoy, Ö., Manolopoulos, Y.: A data mining approach for location prediction in mobile environments. Data & Knowledge Engineering 54(2), 121-146 (2005)