A Semantically Enhanced Label Prediction Method for Imbalanced POI Data Category Distribution

Abstract

POI data play an important role in various location-based services, including navigation, positioning, and local search applications. However, as cities rapidly develop, a substantial amount of new POI data are generated daily, often accompanied by issues with the quality of their labels. Therefore, there is an urgent need to implement intelligent inference and enhancement processing for POI data labels. Conventional neural network models primarily target balanced data distribution, but they fail to address the issue of imbalanced distribution of POI data labels in terms of quantity. Furthermore, most neural network classification models implicitly learn the semantic knowledge of different categories from training datasets, neglecting the explicit semantic information offered by natural language labels. Considering the above problems, several negative samples are introduced for each input to a positive class, thereby transforming the multi-classification task into a binary classification problem. Simultaneously, POI data labels are introduced to provide explicit semantic information, and the semantic relationship between POI data labels and their names is determined using cross-coding. Experiments demonstrate that the macro − F1 score for the test dataset, which contains 75 different categories of POI data, reaches 0.84. This result surpasses the performance of traditional methods, highlighting the effectiveness of the proposed method.

Keywords

• point of interest (POI)
• POI classification
• imbalanced dataset
• semantic enhancement