CONTEXT-AWARE FEATURE QUERY TO IMPROVE THE PREDICTION PERFORMANCE

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.