Context pre-modeling: an empirical analysis for classification based user-centric context-aware predictive modeling

Iqbal H Sarker,Asif Irshad Khan,Hamed Alqahtani,Fawaz Alsolami,Mohammad Khubeb Siddiqui,Yoosef B Abushark

doi:10.1186/s40537-020-00328-3

Abstract

Nowadays, machine learning classification techniques have been successfully used while building data-driven intelligent predictive systems in various application areas including smartphone apps. For an effective context-aware system, context pre-modeling is considered as a key issue and task, as the representation of contextual data directly influences the predictive models. This paper mainly explores the role of major context pre-modeling tasks, such as context vectorization by defining a good numerical measure through transformation and normalization, context generation and extraction by creating new brand principal components, context selection by taking into account a subset of original contexts according to their correlations, and eventually context evaluation, to build effective context-aware predictive models utilizing multi-dimensional contextual data. For creating models, various popular machine learning classification techniques such as decision tree, random forest, k-nearest neighbor, support vector machines, naive Bayes classifier, and deep learning by constructing a neural network of multiple hidden layers, are used in our study. Based on the context pre-modeling tasks and classification methods, we experimentally analyze user-centric smartphone usage behavioral activities utilizing their contextual datasets. The effectiveness of these machine learning context-aware models is examined by considering prediction accuracy, in terms of precision, recall, f-score, and ROC values, and has been made an empirical discussion in various dimensions within the scope of our study.

Highlights

In the context of today’s computing, context-awareness becomes one of the most popular terms, because of the vast usage of Internet of Things (IoT), and lots of applications related to IoT
Experimental results and evaluation we first describe the datasets including contexts, and apps usage, and highlight the evaluation metrics that are taken into account to measure the effectiveness of various machine learning classification models
To show the effect of feature subsets selection with their correlation values, we have shown the prediction results of the resultant context-aware models using various machine learning classification techniques, such as random forest (RF), decision tree (DT), k-nearest neighbor (KNN), naive Bayes (NB), support vector machine (SVM), and artificial neural network (ANN), utilizing the datasets of both the users U1 and U2

Summary

Introduction

In the context of today’s computing, context-awareness becomes one of the most popular terms, because of the vast usage of Internet of Things (IoT), and lots of applications related to IoT. User-centric context-aware predictive model with these apps is needed that considers user’s current needs in different contexts such as a temporal context that represents time-of-the-days or days-of-the-week, one’s working status in workday or holiday, spatial context or user current location, user emotional state, Internet connectivity or Wifi status, or device configuration or relevant status, etc. These contexts may have different types of values depending on individuals’ interests and their behavioral patterns with the surrounding environment and contexts. Classification learning techniques typically build a context-aware model utilizing a given training dataset with contextual information and the resultant predictive model can be used for testing purposes

Objectives

Results

Discussion

Conclusion