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

TL;DR

This paper introduces E-MIIM, an ensemble learning model that improves context-aware mobile interruption management by addressing overfitting issues in existing single decision tree approaches, leading to better prediction accuracy.

Contribution

The paper presents a novel ensemble machine learning model, E-MIIM, that enhances prediction accuracy in context-aware mobile interruption management over traditional single decision tree models.

Findings

E-MIIM outperforms existing MIIM models in prediction accuracy.

The ensemble approach reduces overfitting compared to single decision trees.

Experimental results on real-life datasets validate the effectiveness of E-MIIM.

Abstract

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.