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

• Autorzy: Cudak Magdalena , Piech Mateusz , Marcjan Robert

Identyfikatory

DOI

10.7494/csci.2022.23.2.4086

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

POI; machine learning; geospatial data; data science; first-past-the-post; random forest; point of interest

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (2)
• Strony: 277--296
• Opis fizyczny: Bibliogr. 31 poz., rys., tab.

Twórcy

autor

Cudak Magdalena

• AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Department of Computer Science, Krakow, Poland

autor

Piech Mateusz

• AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Department of Computer Science, Krakow, Poland

autor

Marcjan Robert

• AGH University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Department of Computer Science, Krakow, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).