TY - GEN
T1 - Noise Reduction of Mobile Sensors Data in the Prediction of Demographic Attributes
AU - Hazan, Itay
AU - Shabtai, Asaf
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - In this paper we attempt demonstrate how we can use smartphone sensor data effectively for predicting gender. We specifically focus on sensor data that is assumed to inflict minimal risk to other applications, the system, or the user: Installed Applications, Network Traffic Amount, and Accelerometer readings. We propose several simple heuristics for pre-processing the data and for noise reduction which eventually results in improved accuracy in predicting gender.
AB - In this paper we attempt demonstrate how we can use smartphone sensor data effectively for predicting gender. We specifically focus on sensor data that is assumed to inflict minimal risk to other applications, the system, or the user: Installed Applications, Network Traffic Amount, and Accelerometer readings. We propose several simple heuristics for pre-processing the data and for noise reduction which eventually results in improved accuracy in predicting gender.
KW - Demographic Prediction
KW - Smartphone
UR - http://www.scopus.com/inward/record.url?scp=84961180233&partnerID=8YFLogxK
U2 - 10.1109/MobileSoft.2015.25
DO - 10.1109/MobileSoft.2015.25
M3 - Conference contribution
AN - SCOPUS:84961180233
T3 - Proceedings - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015
SP - 117
EP - 120
BT - Proceedings - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015
PB - Institute of Electrical and Electronics Engineers
T2 - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILESoft 2015
Y2 - 16 May 2015 through 17 May 2015
ER -