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