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.
In this paper we suggest a novel approach that is based on the idea of sensitivity analysis. The proposed approach incrementally asks for features based on the feature acquisition costs and the expected effect each feature can induce on the prediction. Furthermore, the devised method uses back-propagation of gradients and binary representation layers in neural networks to address the computational load as well as scalability concerns. r1 In an earlier work @cite_19 , we introduced the idea of sensitivity analysis as a method for dynamic feature selection. However, in this paper, we extend the idea by considering feature acquisition costs, introducing improvements such as feature encoding, and conducting more detailed experiments and analysis.
@cite_19
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The decision to select which features to use and query can be effectively addressed based on the available features or context. This paper presents a novel approach based on denoising autoencoders and sensitivity analysis in neural networks to efficiently query for unknown features given the context. In this setting, a denoising autoencoder is responsible for handling unknown features. On the other hand, the sensitivity of output predictions with respect to each unknown feature is used as a measure of feature importance. We evaluated the suggested method on human activity recognition and handwritten digit recognition tasks. According to the results, using the proposed method can reduce the number of extracted features in these datasets by approximately 70 and 60 , respectively. This reduction in the number of required features can be crucially important in mobile and battery-powered IoT systems as it reduces the amount of required data acquisition and computational load substantially.