Sparse data classifier based on the first-past-the-post voting system

Point of Interest (POI) is a general term for objects describing places from the real world. The concept of POIs matching, i.e. determining whether two sets of attributes represent the same location, is not a trivial challenge due to the large variety of data sources. The representation of POIs may vary depending on the base in which they are stored. Manual comparison of objects with each other is not achievable in real-time, therefore there are multiple solutions to automatic merging. However there is no efficient solution that includes the deficiencies in the existence of attributes, has been proposed so far. In this paper, we propose the Multilayered Hybrid Classifier which is composed of machine learning and deep learning techniques, supported by the first-past-the-post voting system. We examined different weights for constituencies which were taken into consideration during the majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the current best model - Random Forest, which in its working also base on voting.