Age-Group Classification for Family Members Using Multi-Layered Bayesian Classifier with Gaussian Mixture Model

  • Chuho Yi
  • Seungdo Jeong
  • Kyeong-Soo Han
  • Hankyu Lee
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 240)


This paper proposes a TV viewer age-group classification method for family members based on TV watching history. User profiling based on watching history is very complex and difficult to achieve. To overcome these difficulties, we propose a probabilistic approach that models TV watching history with a Gaussian mixture model (GMM) and implements a feature-selection method that identifies useful features for classifying the appropriate age-group class. Then, to improve the accuracy of age-group classification, a multi-layered Bayesian classifier is applied for demographic analysis. Extensive experiments showed that our multi-layered classifier with GMM is valid. The accuracy of classification was improved when certain features were singled out and demographic properties were applied.


Age-group classification Gaussian mixture model Feature selection 



This work was supported by the Electronics and Telecommunications Research Institute (ETRI) R&D Program of Korea Communications Commission (KCC), Korea [11921-03001, “Development of Beyond Smart TV Technology”].


  1. 1.
    Spangler WE, Gal-Or M, May JH (2003) Using data mining to profile TV viewers. Commun ACM Mob Comput Oppor Chall 46(12):66–72Google Scholar
  2. 2.
    Lee S, Park S, Hong J, Yi C, Jeong S (2012) Inference for the preference of program genre using audience measurement information. In: International conference on information and knowledge, engineering, pp 224–225Google Scholar
  3. 3.
    Wonneberger A, Schoenbach K, Meurs LV (2009) Dynamics of individual television viewing behavior: models, empirical evidence, and a research program. Commun Stud 60(3):235–252CrossRefGoogle Scholar
  4. 4.
    Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396Google Scholar
  5. 5.
    GMMBayes Toolbox, Gaussian mixture model learning and Bayesian classification.

Copyright information

© Springer Science+Business Media Dordrecht(Outside the USA) 2013

Authors and Affiliations

  • Chuho Yi
    • 1
  • Seungdo Jeong
    • 2
  • Kyeong-Soo Han
    • 3
  • Hankyu Lee
    • 3
  1. 1.Research Institute of Electrical and Computer EngineeringHanyang UniversitySeoulKorea
  2. 2.Department of Information and Communication EngineeringHanyang Cyber UniversitySeoulKorea
  3. 3.Electronics and Telecommunications Research InstituteSmart TV Service Research TeamDaejeonKorea

Personalised recommendations