A Rank-Expert Deviation Function to Classify Complex Objects

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This paper proposes a novel function for classifying environmental, social, and socio-environmental objects. It is based on the sum of rank deviations between a given object and a reference object considering the significance of the object’s characteristics (factors). Characteristics are estimated using weight coefficients, which are provided by expertise or another method. A verbal numerical scale is developed to assess the proximity of objects by the numerical value of the deviation function. As is demonstrated below, this function is not a metric in the geometric sense but a proximity function defined in multidimensional scaling theory. As illustrative examples, the values of the deviation function are calculated for two applications: an environmental problem of comparing the vulnerability of territories to accidental oil spills and an economic problem of choosing real estate objects to purchase. A recommended sequence with a set of procedures based on the deviation function is presented to solve these problems.

Sobre autores

V. Korobov

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: szoioran@mail.ru
Moscow, Russia

A. Tutygin

Laverov Federal Center for Integrated Arctic Research, Ural Branch, Russian Academy of Sciences

Email: andgt64@yandex.ru
Arkhangelsk, Russia

A. Lokhov

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: a.s.lohov@yandex.ru
Moscow, Russia

Bibliografia

  1. Хантингтон С. Столкновение цивилизаций. – М.: АСТ: Астрель, 2011. – 571 с. [Huntington, S.P. The Clash of Civilizations and the Remaking of World Order. – New York: Simon & Schuster, 2011. – 368 p.]
  2. Линней К. Философия ботаники. – М.: Наука, 1989. – 456 с. [Linnaeus, C. Linnaeus' Philosophia Botanica. – Oxford: Oxford University Press, 2005. – 428 p.]
  3. Hamming, R.W. Error-Detecting and Error-Correcting Codes // Bell System Technical Journal. – 1950. – Vol. 29, no. 2. – P. 147–160.
  4. Deza, M., Deza, E. Encyclopedia of Distances. – Berlin–Heidelberg: Springer-Verlag, 2009. – 590 p.
  5. Guttman, L. A General Nonmetric Technique for Finding the Smallest Coordinate Space for a Configuration of Points // Psychometrika. – 1968. – Vol. 23, no. 4. – P. 469–506.
  6. Толстова Ю.Н. Основы многомерного шкалирования: учебное пособие. – М.: КДУ, 2006. – 160 с. [Tolstova Yu.N. Osnovy mnogomernogo shkalirovaniya: uchebnoe posobie. – M.: KDU, 2006. – 160 p. (In Russian)]
  7. Everitt, B.S., Landau, S., Leese, M., Stahl, D. Cluster Analysis: Fifth Edition. – Hoboken: John Wiley & Sons, 2011. – 330 p. – doi: 10.1002/9780470977811.
  8. Baccour, L. Amended Fused TOPSIS-VIKOR for Classification (ATOVIC) Applied to Some UCI Data Sets // Expert Systems with Applications. – 2018. – Vol. 99. – Р. 115–125. – doi: 10.1016/j.eswa.2018.01.025.
  9. Yusuf H., Panoutsos G. Multi-criteria Decision Making Using Fuzzy Logic and ATOVIC with Application to Manufacturing // IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). – Glasgow, UK, 2020. – P. 1–7. – doi: 10.1109/FUZZ48607.2020.9177772.
  10. Taunk, K., De, S., Verma, S., Swetapadma, A. A Brief Review of Nearest Neighbor Algorithm for Learning and Classification // International Conference on Intelligent Computing and Control Systems (ICCS). – Madurai, India, 2019. – P. 1255–1260. – doi: 10.1109/ICCS45141.2019.9065747.
  11. Yang F.J. An Implementation of Naive Bayes Classifier // International Conference on Computational Science and Computational Intelligence (CSCI). – Las Vegas, NV, USA, 2018. – P. 301–306. – doi: 10.1109/CSCI46756.2018.00065.
  12. Коробов В.Б., Тутыгин А.Г. Классификационные методы решения эколого-экономических задач. – Архангельск: Поморский университет, 2010. – 310 с. [Korobov, V.B., Tutygin, A.G. Klassifikatsionnye metody resheniya ehkologo-ehkonomicheskikh zadach. – Arkhangel'sk: Pomorskii universitet, 2010. – 310 p. (In Russian)]
  13. Безуглая Э.Ю. Мониторинг состояния атмосферы в городах. – Л.: Гидрометеоиздат, 1986. – 200 с. [Bezuglaya, Eh.Yu. Monitoring sostoyaniya atmosfery v gorodakh. – L.: Gidrometeoizdat. – 1986. – 200 p. (In Russian)]
  14. Лохов А.С., Губайдуллин М.Г., Коробов В.Б., Тутыгин А.Г. Географо-экологическое районирование трассы нефтепровода по степени опасности воздействия на окружающую среду при аварийных разливах нефти в Арктике // Теоретическая и прикладная экология. – 2020. – № 4. – С. 43–48. – doi: 10.25750/1995-4301-2020-4-045-050. [Lokhov, A.S., Gubaidullin, M.G., Korobov, V.B, Tutygin, A.G. Geographical and Ecological Land Zoning of Onshore Oil Pipeline Location by Level of Hazard to Environment from Emergency Oil Spills in Arctic Region // Theoretical and Applied Ecology. – 2020. – No. 4. – P. 43–48. (In Russian)]
  15. Коробов В.Б., Кочуров Б.И., Тутыгин А.Г. Методология районирования сложных географо-экологических объектов экспертно-статистическими методами // Проблемы региональной экологии. – 2020. – № 5. – C. 42–48. [Korobov, V.B., Kochurov, B.I., Tutigin, A.G. Methodology of Zoning of Complex Geographic and Ecological Objects Using Expert Statistical Methods // Problemy regional'noi ehkologii. – 2020. – No. 5. – P. 42–48. (In Russian)]

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