ContextPCA: Predicting Context-Aware Smartphone Apps Usage Based On Machine Learning Techniques

Symmetry

YoosefB. Abushark¹  AsifIrshad Khan¹  IqbalH. Sarker² 

[1] Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne VIC-3122, Australia;

关键词: mobile data analytics;    machine learning;    principal component analysis;    classification;    decision tree;    context-aware computing;   

DOI  :  10.3390/sym12040499

来源: DOAJ

【 摘 要 】

This paper mainly formulates the problem of predicting context-aware smartphone apps usage based on machine learning techniques. In the real world, people use various kinds of smartphone apps differently in different contexts that include both the user-centric context and device-centric context. In the area of artificial intelligence and machine learning, decision tree model is one of the most popular approaches for predicting context-aware smartphone usage. However, real-life smartphone apps usage data may contain higher dimensions of contexts, which may cause several issues such as increases model complexity, may arise over-fitting problem, and consequently decreases the prediction accuracy of the context-aware model. In order to address these issues, in this paper, we present an effective principal component analysis (PCA) based context-aware smartphone apps prediction model, “ContextPCA” using decision tree machine learning classification technique. PCA is an unsupervised machine learning technique that can be used to separate symmetric and asymmetric components, and has been adopted in our “ContextPCA” model, in order to reduce the context dimensions of the original data set. The experimental results on smartphone apps usage datasets show that “ContextPCA” model effectively predicts context-aware smartphone apps in terms of precision, recall, f-score and ROC values in various test cases.

【 授权许可】

Unknown