Location semantics prediction for living analytics by mining smartphone data

Chi Min Huang, Josh Jia Ching Ying, Vincent Shin-Mu Tseng, Zhi Hua Zhou

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

8 Scopus citations

Abstract

Automatic location semantics prediction for living analytics based on smartphone data has attracted extensive attention in just recent years. Basically, this task can be formulated as a multi-class classification problem, where different location/places are regarded as different labels. Previous studies were mostly based on common classification techniques directly, neglecting the critical challenging issue of class imbalance in such a problem (e.g., people go to offices much more often than they go to cinemas). It is also noteworthy that in contrast to common multi-class problems where the classes can be treated independently and interchangeably, the places for labeling usually have important correlations, which should be taken account in the classification/labeling process. Moreover, several activities may occur in the same place and thus the same place label might convey different semantics. In this paper, we address the above issues for location semantics prediction by proposing the FS-Mining (Frame-based Semantics Mining) approach. We treat the raw sensor data in the smartphone as a sequence of short and non-overlapping frames, based on which the user behavior at each place can be characterized and the place semantics can be modeled. To deal with the issues of label relation and class imbalance, a multi-level classification model with class-split and class-merge mechanisms was also developed. An ensemble strategy was also employed to further improve the performance. Experiments on the dataset of Nokia Mobile Data Challenge [1] demonstrate promising performances for the FS-Mining approach.

Original languageEnglish
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages527-533
Number of pages7
ISBN (Electronic)9781479969913
DOIs
StatePublished - 10 Mar 2014
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: 30 Oct 20141 Nov 2014

Publication series

NameDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Conference

Conference2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
CountryChina
CityShanghai
Period30/10/141/11/14

Keywords

  • Automatic place labeling
  • Class imbalance
  • Location semantics prediction
  • Multi-level classification
  • User behavior analysis

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  • Cite this

    Huang, C. M., Ying, J. J. C., Tseng, V. S-M., & Zhou, Z. H. (2014). Location semantics prediction for living analytics by mining smartphone data. In G. Karypis, L. Cao, W. Wang, & I. King (Eds.), DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics (pp. 527-533). [7058122] (DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2014.7058122