In real-world scenarios, different features have different acquisition costs at test time which necessitates cost-aware methods to optimize the cost and performance tradeoff. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers. In the proposed architecture, a denoising autoencoder is used to handle unknown features (i.e., features that are yet to be acquired), and the sensitivity of predictions with respect to each unknown feature is used as a context-dependent measure of informativeness. We evaluated the proposed method on eight different real-world data sets as well as one synthesized data set and compared its performance with several other approaches in the literature. According to the results, the suggested method is capable of efficiently acquiring features at test time in a cost- and context-aware fashion.
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.