Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage

Iqbal H Sarker,A S M Kayes,Paul Watters

doi:10.1186/s40537-019-0219-y

Abstract

Due to the increasing popularity of recent advanced features and context-awareness in smart mobile phones, the contextual data relevant to users’ diverse activities with their phones are recorded through the device logs. Modeling and predicting individual’s smartphone usage based on contexts, such as temporal, spatial, or social information, can be used to build various context-aware personalized systems. In order to intelligently assist them, a machine learning classifier based usage prediction model for individual users’ is the key. Thus, we aim to analyze the effectiveness of various machine learning classification models for predicting personalized usage utilizing individual’s phone log data. In our context-aware analysis, we first employ ten classic and well-known machine learning classification techniques, such as ZeroR, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Adaptive Boosting, Repeated Incremental Pruning to Produce Error Reduction, Ripple Down Rule Learner, and Logistic Regression classifiers. We also present the empirical evaluations of Artificial Neural Network based classification model, which is frequently used in deep learning and make comparative analysis in our context-aware study. The effectiveness of these classifier based context-aware models is examined by conducting a range of experiments on the real mobile phone datasets collected from individual users. The overall experimental results and discussions can help both the researchers and applications developers to design and build intelligent context-aware systems for smartphone users.

Highlights

Nowadays, smartphones are considered as the most useful and essential devices of our daily life, in which individuals’ around the world communicate with one another for various purposes
As deep learning has become very popular in recent days and used in various application areas, briefly discussed in “Machine learning classifiers: background and related work” section, we present empirical evaluations with Artificial Neural Network (ANN) learning model, which is frequently used in deep learning for making comparative analysis in our context-aware study
We have shown the experimental results of ZeroR, Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Adaptive Boosting (AdaBoost), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), Ripple Down Rule Learner (RIDOR), Logistic Regression (LR), and Artificial Neural Network (ANN) classifiers based smartphone usage prediction models

Summary

Introduction

Smartphones are considered as the most useful and essential devices of our daily life, in which individuals’ around the world communicate with one another for various purposes. Individual’s phone call activity [10, 11] (e.g., answer, decline, missed, or making outgoing call) model based on relevant contexts utilizing phone call log data, can be used to provide personalized phone call services to intelligently manage the call interruptions. Such call interruptions mostly cause by the incoming calls in an inappropriate time (e.g., professional meeting in the morning). In order to provide such context-aware personalized services intelligently, a machine learning classifier based usage prediction model utilizing the relevant contextual phone log data is the key. We take into account individual’s phone call activities in relevant contexts, and corresponding experiments using machine learning classifiers throughout the paper

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Results

Discussion

Conclusion