SPARSE DATA CLASSIFIER BASED ON FIRST-PAST-THE-POST VOTING SYSTEM

Abstract

A point of interest (POI) is a general term for objects that describe places from the real world. The concept of POI 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 representations of POIs may vary depending on the basis of how they are stored. A manual comparison of objects is not achievable in real time; therefore, there are multiple solutions for automatic merging. However, there is no yet the efficient solution solves the missing of the attributes. In this paper, we propose a multi-layered hybrid classifier that is composed of machine-learning and deep-learning techniques and supported by a first-past-the-post voting system. We examined different weights for the constituencies that were taken into consideration during a majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the best current model (random forest), which also is based on voting.