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The use of rough rules in the selection of topographic objects for generalizing geographical information

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Selection is a key element of the cartographic generalisation process, often being its first stage. On the other hand it is a component of other generalisation operators, such as simplification. One of the approaches used in generalization is the condition-action approach. The author uses a condition-action approach based on three types of rough logics (Rough Set Theory (RST), Dominance-Based Rough Set Theory (DRST) and Fuzzy-Rough Set Theory (FRST)), checking the possibility of their use in the process of selecting topographic objects (buildings, roads, rivers) and comparing the obtained results. The complexity of the decision system (the number of rules and their conditions) and its effectiveness are assessed, both in terms of quantity and quality – through visual assessment. The conducted research indicates the advantage of the DRST and RST approaches (with the CN2 algorithm) due to the quality of the obtained selection, the greater simplicity of the decision system, and better refined IT tools enabling the use of these systems. At this stage, the FRST approach, which is characterised by the highest complexity of created rules and the worst selection results, is not recommended. Particular approaches have limitations resulting from the need to select appropriate measurement scales for the attributes used in them. Special attention should be paid to the selection of network objects, in which the use of only a condition-action approach, without maintaining consistency of the network, may not produce the desired results. Unlike approaches based on classical logic, rough approaches allow the use of incomplete or contradictory information. The proposed tools can (in their current form) find an auxiliary use in the selection of topographic objects, and potentially also in other generalisation operators.
Rocznik
Strony
1--15
Opis fizyczny
Bibliogr. 25 poz., mapy, rys., tab., wykr.
Twórcy
  • Warsaw University of Technology, Faculty of Geodesy and Cartography
Bibliografia
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  • Browne, M.W., 2000, Cross-validation methods. “Journal of Mathematical Psychology” Vol. 44, no. 1,pp. 108–132.
  • Cornelis C., Martín G.H., Jensen R., Ślȩzak D., 2008, Feature selection with fuzzy decision reducts. In: Proceedings of the International Conference on Rough Sets and Knowledge Technology, Chengdu, China, 17–18 May 2008, Berlin – Heidelberg: Springer, pp. 284–291.
  • Dubois D., Prade H., 1990, Rough fuzzy sets and fuzzy rough sets. “Intern. Journal of General Systems” Vol, 17, no. 2/3, pp. 191–209.
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  • Fiedukowicz A., 2013a, Construction of fuzzy interference system for generalization of geographic information – selection of roads segments. In: “Geoinformatica Polonica” Vol. 12, pp. 53–62.
  • Fiedukowicz A., 2013b, Wykorzystanie zbiorów przybliżonych do pozyskiwania wiedzy i budowy reguł systemu generalizacji informacji geograficznej. „Roczniki Geomatyki” T. 11, nr 2(59), pp. 33–46.
  • Fiedukowicz A., 2015a, Fuzzy rough sets theory reducts for quantitative decisions – Approach for spatial data generalization. In: Pattern Recognition and Machine Intelligence. Proceedings. Eds. M. Kryszkiewicz end al. „Lecture Notes in Computer Science” Vol. 9124, pp. 314–323.
  • Fiedukowicz A., 2015b, Redukcja wymiarowości problemu – ograniczenie liczby cech. In: Wybrane metody eksploracyjnej analizy danych przestrzennych (Spatial Data Mining). Eds. A. Fiedukowicz, J. Gąsiorowski, R. Olszewski. Warszawa: Wydział Geodezji i Kartografii Politechniki Warszawskiej.
  • Fiedukowicz A., 2017, Metodyka wykorzystania reduktów i reguł przybliżonych w procesie generalizacji informacji geograficznej. PhD. dissertation, Warsaw University of Technology, Faculty of Geodesy and Cartography.
  • Fiedukowicz A., 2020, The role of spatial context information in the generalization of geographic information: Using reducts to indicate relevant attributes. “ISPRS International Journal of Geo-Information” Vol. 9, no. 1, 37.
  • Greco S., Matarazzo B., Słowiński R., 2001, Rough sets theory for multicriteria decision analysis. “European Journal of Operational Research” Vol. 129, pp. 1–47.
  • Harrie L., Weibel R., 2007, Modelling the overall proces of generalization. In: Generalization of Geographic Information. Amsterdam: Elsevier Science BV, pp. 67–87.
  • Łukasiewicz J., 1958, Elementy logiki matematycznej. Warszawa: Państwowe Wydawnictwo Naukowe.
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Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a43f369-9d0c-4670-ac1b-5d989d40f4d6
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