Deep Learning Applied to Mobile Phone Data for Individual Income Classification

Abstract

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 socioeconomic 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.

Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories

“…Each recorded event reported the coverage area of the principal antenna and the activity’s time stamp. CDR data have been widely used in research studies related to human mobility and trajectory analysis [ 34 , 35 , 36 , 37 ].…”

Deep Learning in Mobile and Wireless Networking: A Survey

“…Ordóñez and Roggen architect an advanced ConvLSTM to fuse data gathered from multiple sensors and perform activity recognition [112]. By leveraging CNN and LSTM structures, ConvLSTMs can automatically compress spatio-temporal sensor data into low-dimensional [236] Mobile ear Edge-based CNN Jindal [237] Heart rate prediction Cloud-based DBN Kim et al [238] Cytopathology classification Cloud-based CNN Sathyanarayana et al [239] Sleep quality prediction Cloud-based MLP, CNN, LSTM Li and Trocan [240] Health conditions analysis Cloud-based Stacked AE Hosseini et al [241] Epileptogenicity localisation Cloud-based CNN Stamate et al [242] Parkinson's symptoms management Cloud-based MLP Quisel et al [243] Mobile health data analysis Cloud-based CNN, RNN Khan et al [244] Respiration [250] Facial recognition Cloud-based CNN Wu et al [291] Mobile visual search Edge-based CNN Rao et al [251] Mobile augmented reality Edge-based CNN Ohara et al [290] WiFi-driven indoor change detection Cloud-based CNN,LSTM Zeng et al [252] Activity recognition Cloud-based CNN, RBM Almaslukh et al [253] Activity recognition Cloud-based AE Li et al [254] RFID-based activity recognition Cloud-based CNN Bhattacharya and Lane [255] Smart watch-based activity recognition Edge-based RBM Antreas and Angelov [256] Mobile surveillance system Edge-based & Cloud based CNN Ordóñez and Roggen [112] Activity recognition Cloud-based ConvLSTM Wang et al [257] Gesture recognition Edge-based CNN, RNN Gao et al [258] Eating detection Cloud-based DBM, MLP Zhu et al [259] User energy expenditure estimation Cloud-based CNN, MLP Sundsøy et al [260] Individual income classification Cloud-based MLP Chen and Xue [261] Activity recognition Cloud-based CNN Ha and Choi [262] Activity recognition Cloud-based CNN Edel and Köppe [263] Activity recognition Edge-based Binarized-LSTM Okita and Inoue [266] Multiple overlapping activities recognition Cloud-based CNN+LSTM Alsheikh et al…”

LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

“…In order to present meaningful findings, it is indeed important, especially when dealing with wide territories, to make use of a sufficiently large and complete dataset, whose trajectories redundantly cover the study area. CDRs have been widely used in human mobility studies [74][75][76][77], reporting the detected mobile phone activities enriched with the time stamp and the position of the device in terms of the coverage area of the principal antenna. We only took into account short-term visitors, recorded to be located in the country for a maximum of two weeks.…”