Comparison of Feature Extraction Methods and Predictors for Income Inference
This work addresses income inference for users based on mobile phone data, but it is incremental as it compares existing methods without introducing new paradigms.
The paper tackled the problem of inferring users' income levels from mobile phone data by comparing feature extraction methods and predictors, finding that a Bayesian method based on the communication graph outperformed standard algorithms using node-based features.
Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users' socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users' income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using node-based features.