Feature fusion for mobile usage prediction using rank-score characteristics

The aim of this paper is to investigate feature fusion problem for mobile usage prediction using combinatorial fusion analysis (CFA). CFA uses the rank-score characteristics (RSC) function to guide the process of selecting score-based fusion (SF) or rank-based fusion (RF). We study the feature fusion of two mobile adaptive user interface applications: dynamic shortcuts for application launching and dynamic contact list, which improve the usability of mobile devices through usage predication. Our results confirm that for mobile usage prediction RSC function is useful for feature fusion decision. It also proves that RF outperforms SF when the features have unique scoring behavior and relatively high performance.