Identifying duplicated ads on property selling and renting websites

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on Information Technologies in Business and Industries, ITBI 2019

Год издания: 2019

Идентификатор DOI: 10.1088/1742-6596/1333/7/072025

Аннотация: The article presents a solution for the problem of identifying duplicated ads on property selling websites. This task is formulated in the form of a classification problem: The input parameters are identified then divided into basic and non-basic, as well as a class-forming feature. It is also necessary to consider the preliminary data of processed property objects, which is necessary for proper application of the classification methods. The following is a brief review of chosen modern algorithms for solving classification problems, namely: Decision trees, artificial neural networks, logistic regression. As a result of experiments, it was revealed that Artificial neural network gives the most accurate result therefore, this algorithm is suitable for the solution of the stated problem. © Published under licence by IOP Publishing Ltd.

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Издание

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1333, Is. 7

Номера страниц: 72025

ISSN журнала: 17426588

Издатель: Institute of Physics Publishing

Авторы

  • Tynchenko V.S. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, KrasnoyarskyRabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Kukartsev V.V. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, KrasnoyarskyRabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Tynchenko V.V. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, KrasnoyarskyRabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Bukhtoyarov V.V. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, KrasnoyarskyRabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Chzhan E.A. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation)
  • Kukartsev V.A. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation)
  • Boyko A.A. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, KrasnoyarskyRabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)

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