Estimating residual value of heavy construction equipment using ensemble learning
Чланак у часопису (Рецензирана верзија)
Метаподаци
Приказ свих података о документуАпстракт
Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.
Кључне речи:
Artificial intelligence and machine learning / Business management / Decision making / Quantitative methodsИзвор:
Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers, 2021, 147, 7Издавач:
- http://cedb.asce.org
Колекције
Институција/група
GraFarTY - JOUR AU - Milošević, Igor AU - Kovačević, Miloš AU - Petronijević, Predrag PY - 2021 UR - https://grafar.grf.bg.ac.rs/handle/123456789/2422 AB - Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012. PB - http://cedb.asce.org T2 - Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers T1 - Estimating residual value of heavy construction equipment using ensemble learning IS - 7 VL - 147 DO - 10.1061/(ASCE)CO.1943-7862.0002088 ER -
@article{ author = "Milošević, Igor and Kovačević, Miloš and Petronijević, Predrag", year = "2021", abstract = "Knowing the right moment for the sale of used heavy construction equipment is important information for every construction company. The proposed methodology uses ensemble machine learning techniques to estimate the price (residual value) of used heavy equipment in both the present and the near future. Each machine in the model is represented with four groups of attributes: age and mechanical (describing the machine) and geographical and economic (describing the target market). The research suggests that the ensemble model based on random forest, light gradient boosting, and neural network members, as well as support vector regression as a decision unit, gives better estimates than the traditional regression or individual machine learning models. The model is built and verified on a large data set of 500,000 machines advertised in 50 US states from 1989 to 2012.", publisher = "http://cedb.asce.org", journal = "Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers", title = "Estimating residual value of heavy construction equipment using ensemble learning", number = "7", volume = "147", doi = "10.1061/(ASCE)CO.1943-7862.0002088" }
Milošević, I., Kovačević, M.,& Petronijević, P.. (2021). Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers http://cedb.asce.org., 147(7). https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088
Milošević I, Kovačević M, Petronijević P. Estimating residual value of heavy construction equipment using ensemble learning. in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers. 2021;147(7). doi:10.1061/(ASCE)CO.1943-7862.0002088 .
Milošević, Igor, Kovačević, Miloš, Petronijević, Predrag, "Estimating residual value of heavy construction equipment using ensemble learning" in Journal of Construction Engineering and Management. ASCE / American Society of Civil Engineers, 147, no. 7 (2021), https://doi.org/10.1061/(ASCE)CO.1943-7862.0002088 . .