An Ensemble Learning Scheme for Indoor-Outdoor Classification Based on KPIs of LTE Network

Wireless Big Data has aroused extensive attention, as mass mobile devices have been developed and deployed for the upcoming 5G era. The context information of these devices is of importance for personalized services in a smart environment. Nevertheless, the constant change of scenes challenges to the network operator. In this paper, we propose an ensemble learning scheme for indoor-outdoor classification for a typical urban area, based on the cellular data captured in a commercial LTE network. The variables are extracted by network key performance indicators (KPIs) and radio propagation knowledge. Based on these main variables, the decision trees grow and split by the Gini index of sampled features. Then, all decision trees are assembled as weak learners to build the ensemble scheme, thus improving the discrimination ability. The self-validation results show the ensemble model achieves extreme accurate (with an out-of-bag error lower than 1%) classification for indoor and outdoor environments. Moreover, the prominent variables are selected based on the variable importance of in the initial training. The reconfigured model based on fewer variables and less weak learners also gains the highest accuracy and relative short compute time, compared with other classical machine learning methods.