Deep Learning Applied to Mobile Phone Data for Individual Income Classification
Pål Sundsøy, Johannes Bjelland, Bjørn-Atle Reme, Asif M.Iqbal, Eaman Jahani
Available Online January 2016.
- https://doi.org/10.2991/icaita-16.2016.24How to use a DOI?
- deep learning; mobile phone data; poverty;household income; Big Data Analytics; Machine learning; Asia, Mobile network operator; metadata; algorithms
- Deep learning has in recent years brought breakthroughs in several domains, most notably voice and image recognition. In this work we extend deep learning into a new application domain - namely classification on mobile phone datasets. Classic machine learning methods have produced good results in telecom prediction tasks, but are underutilized due to resource-intensive and domain-specific feature engineering. Moreover, traditional machine learning algorithms require separate feature engineering in different countries. In this work, we show how socio-economic status in large de-identified mobile phone datasets can be accurately classified using deep learning, thus avoiding the cumbersome and manual feature engineering process. We implement a simple deep learning architecture and compare it with traditional data mining models as our benchmarks. On average our model achieves 77% AUC on test data using location traces as the sole input. In contrast, the benchmarked state-of-the-art data mining models include various feature categories such as basic phone usage, top-up pattern, handset type, social network structure and individual mobility. The traditional machine learning models achieve 72% AUC in the best-case scenario. We believe these results are encouraging since average regional household income is an important input to a wide range of economic policies. In underdeveloped countries reliable statistics of income is often lacking, not frequently updated, and is rarely fine-grained to sub-regions of the country. Making income prediction simpler and more efficient can be of great help to policy makers and charity organizations – which will ultimately benefit the poor.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Pål Sundsøy AU - Johannes Bjelland AU - Bjørn-Atle Reme AU - Asif M.Iqbal AU - Eaman Jahani PY - 2016/01 DA - 2016/01 TI - Deep Learning Applied to Mobile Phone Data for Individual Income Classification BT - 2016 International Conference on Artificial Intelligence: Technologies and Applications PB - Atlantis Press SP - 96 EP - 99 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-16.2016.24 DO - https://doi.org/10.2991/icaita-16.2016.24 ID - Sundsøy2016/01 ER -