Target inductive methods for zero-shot regression
This work addresses the specific problem of air pollution prediction for environmental monitoring, but it is incremental as it adapts existing zero-shot concepts from classification to regression.
The paper tackles the problem of predicting air pollutants in new meteorological stations without training data, a zero-shot regression scenario, by proposing two methods that use side information about station surroundings, and shows that both outperform a baseline, with the parameter learning approach performing best.
This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings). Frequently, the surrounding information is not considered in the learning process. This information is known beforehand in the absence of unobserved weather conditions and remains constant for the same station. Considering the surrounding information as side information facilitates the generalization for predicting pollutants in new stations, leading to a zero-shot regression scenario. Available methods in zero-shot typically lean towards classification, and are not easily extensible to regression. This paper proposes two zero-shot methods for regression. The first method is a similarity based approach that learns models from features and aggregates them using side information. However, potential knowledge of the feature models may be lost in the aggregation. The second method overcomes this drawback by replacing the aggregation procedure and learning the correspondence between side information and feature-induced models, instead. Both proposals are compared with a baseline procedure using artificial datasets, UCI repository communities and crime datasets, and the pollutants. Both approaches outperform the baseline method, but the parameter learning approach manifests its superiority over the similarity based method.