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Using Graphical Features To Improve Demographic Prediction From Smart Phone Data

Published: 14 May 2017 Publication History

Abstract

Demographic information such as gender, age, ethnicity, level of education, disabilities, employment, and socio-economic status are important in the area of social science, survey and marketing. But it is difficult to obtain the demographic information from users due to reluctance of users to participate and low response rate. Through automated demographics prediction from smart phone sensor data, researchers can obtain this valuable information in a nonintrusive and cost-effective manner. We approach the problem of demographic prediction, namely, classification of gender, age group and job type, through the use of a graphical feature based framework. The framework represents information collected from sensor networks as graphs, extracts useful and relevant graphical features, and predicts demographic information. We evaluated our approach on the Nokia Mobile Phone dataset for the three classification tasks: gender, age-group and job-type. Our approach produced comparable results with most of the state of the art methods while having the additional advantage of general applicability to sensor networks without using sophisticated and application-specific feature generation techniques, background knowledge and special techniques to address class imbalance.

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Cited By

View all
  • (2022)Predicting age and gender from network telemetry: Implications for privacy and impact on policyPLOS ONE10.1371/journal.pone.027171417:7(e0271714)Online publication date: 21-Jul-2022
  • (2019)Improving IoT Predictions through the Identification of Graphical FeaturesSensors10.3390/s1915325019:15(3250)Online publication date: 24-Jul-2019
  • (2018)Activity Recognition Using Graphical Features from Smart Phone SensorInternet of Things – ICIOT 201810.1007/978-3-319-94370-1_4(45-55)Online publication date: 17-Jun-2018

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Published In

cover image ACM Conferences
NDA'17: Proceedings of the 2nd International Workshop on Network Data Analytics
May 2017
46 pages
ISBN:9781450349901
DOI:10.1145/3068943
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 May 2017

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Author Tags

  1. Graph mining
  2. demographic prediction
  3. feature extraction
  4. graph representation

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Overall Acceptance Rate 4 of 8 submissions, 50%

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Cited By

View all
  • (2022)Predicting age and gender from network telemetry: Implications for privacy and impact on policyPLOS ONE10.1371/journal.pone.027171417:7(e0271714)Online publication date: 21-Jul-2022
  • (2019)Improving IoT Predictions through the Identification of Graphical FeaturesSensors10.3390/s1915325019:15(3250)Online publication date: 24-Jul-2019
  • (2018)Activity Recognition Using Graphical Features from Smart Phone SensorInternet of Things – ICIOT 201810.1007/978-3-319-94370-1_4(45-55)Online publication date: 17-Jun-2018

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