A spatial dependency based reinforcement learning model for selecting features in spatial classification

Traditional feature-based classification methods require objects to have the explicit, independent, and identifiable set of features, while most geo-referenced objects do not have the explicit features required by classifiers. Therefore, developing classificatory features under geospatial context is a prerequisite for effective spatial classification. Considering the spatial dependency, objects are correlated with each other, and for the object of interest its features (e.g., the distribution of neighboring objects) exist in a wide range of neighboring areas. However, the uncertainty of neighborhood size makes the dimensionality of potential feature set particularly high for spatial classification. Therefore, we propose a new model to automatically select a subset of spatially explicit features through continuous decision making by multiple agents in reinforcement learning (RL). A novel reward mechanism is developed to feed the knowledge of the downstream classification task back to the loop of feature selection. Through extensive experiments with facility points-of-interest datasets, we demonstrate that the subset of classificatory features selected by our RL model can help significantly improve the accuracy of spatial classification. Moreover, our feature selection has potential explainability for the spatial classification rules as it can determine the neighboring areas which have an impact on the classification result.