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Nowadays, mobile telephony interruptions in our daily life activities arecommon because of the inappropriate ringing notifications of incoming phonecalls in different contexts. Such interruptions may impact on the workattention not only for the mobile phone owners but also the surrounding people.Decision tree is the most popular machine learning classification techniquethat is used in existing context-aware mobile intelligent interruptionmanagement (MIIM) model to overcome such issues. However, a single decisiontree based context-aware model may cause overfitting problem and thus decreasethe prediction accuracy of the inferred model. Therefore, in this paper, wepropose an ensemble machine learning based context-aware mobile telephony modelfor the purpose of intelligent interruption management by taking into accountmulti-dimensional contexts and name it "E-MIIM". The experimental results onindividuals' real life mobile telephony datasets show that our E-MIIM model ismore effective and outperforms existing MIIM model for predicting and managingindividual's mobile telephony interruptions based on their relevant contextualinformation.

E-MIIM: An Ensemble Learning based Context-Aware Mobile Telephony Model for Intelligent Interruption Management