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

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.