Test time feature ordering with FOCUS: interactive predictions with minimal user burden

Predictive algorithms are a critical part of the ubiquitous computing vision, enabling appropriate action on behalf of users. A common class of algorithms, which has seen uptake in ubiquitous computing, is supervised machine learning algorithms. Such algorithms are trained to make predictions based on a set of features (selected at training time). However, features needed at prediction time (such as mobile information that impacts battery life, or information collected from users via experience sampling) may be costly to collect. In addition, both cost and value of a feature may change dynamically based on real-world context (such as battery life or user location) and prediction context (what features are already known, and what their values are). We contribute a framework for dynamically trading off feature cost against prediction quality at prediction time. We demonstrate this work in the context of three prediction tasks: providing prospective tenants estimates for energy costs in potential homes, estimating momentary stress levels from both sensed and user-provided mobile data, and classifying images to facilitate opportunistic device interactions. Our results show that while our approach to cost-sensitive feature selection is up to 45% less costly than competing approaches, error rates are equivalent or better.