Noisy instance in mobile phone data is an important issue for modeling user phone call behavior, with many potential negative consequences. The accuracy of prediction may decrease, thereby increasing the complexity of inferred models and the number of training samples needed. In this paper, we present an effective phone call prediction model based on noisy mobile phone data in order to improve the prediction accuracy for individual mobile phone users. Experimental results on the real phone call log datasets show the effectiveness of our prediction model for individual mobile phone users.
|Title of host publication||UbiComp 2017 Adjunct - Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing|
|Place of Publication||United States|
|Publisher||Association for Computing Machinery, Inc (ACM)|
|Number of pages||4|
|Publication status||Published - 2017|
|Event||2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017 - Grand Wailea, Wailea, Maui, United States|
Duration: 11 Sep 2017 → 15 Sep 2017
http://www.ubicomp.org/ubicomp2017/index.html (Conference website)
https://dl.acm.org/citation.cfm?id=3123024&picked=prox (Conference proceedings)
|Conference||2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017|
|Period||11/09/17 → 15/09/17|
|Other||The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UBICOMP 2017) is the premier outlet for novel research contributions in ubiquitous and pervasive computing systems. It is the result of a merger of the two most renowned conferences in the field: Pervasive and UBICOMP. |
The 2017 International Symposium on Wearable Computers (ISWC 2017) is the premier forum for wearable computing and issues related to on-body and worn mobile technologies. It brings together researchers, product vendors, fashion designers, textile manufacturers, users, and related professionals.
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