Categories of Point of Interest (POI) facilitate location-based services from many aspects like location search and place recommendation[6]. However, POI categories are often incomplete and new POIs are increasing, this rises the problem of automatic POI classification. Current POI classification methods suffer from two problems: lack of textual information about POIs and not leveraging the hierarchical structure of the categories. In this paper, we propose an Ensemble POI Hierarchical Classification framework (EHC) consisting of three components: Textual and Geographic Feature Extraction, Hierarchical Classifier, and Soft Voting Ensemble Model. We conduct extensive experiments to demonstrate the effectiveness of our framework.
CITATION STYLE
Liu, S., Yu, J., Li, J., & Hou, L. (2020). Geographical information enhanced poi hierarchical classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12432 LNCS, pp. 108–119). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60029-7_10
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