  Nowadays, mobile telephony interruptions in our daily life activities are
common because of the inappropriate ringing notifications of incoming phone
calls in different contexts. Such interruptions may impact on the work
attention not only for the mobile phone owners but also the surrounding people.
Decision tree is the most popular machine learning classification technique
that is used in existing context-aware mobile intelligent interruption
management (MIIM) model to overcome such issues. However, a single decision
tree based context-aware model may cause overfitting problem and thus decrease
the prediction accuracy of the inferred model. Therefore, in this paper, we
propose an ensemble machine learning based context-aware mobile telephony model
for the purpose of intelligent interruption management by taking into account
multi-dimensional contexts and name it "E-MIIM". The experimental results on
individuals' real life mobile telephony datasets show that our E-MIIM model is
more effective and outperforms existing MIIM model for predicting and managing
individual's mobile telephony interruptions based on their relevant contextual
information.
