Application of machine learning methods for risk analysis of unfavorable outcome of government procurement procedure in building and grounds maintenance domain
| Autor: | Elena Kripak, Jenny V. Domashova |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Risk analysis
Association rule learning Government procurement business.industry Computer science Machine learning computer.software_genre Domain (software engineering) Procurement Risk analysis (engineering) Collusion Information system General Earth and Planetary Sciences Artificial intelligence business Cluster analysis computer General Environmental Science |
| Zdroj: | BICA |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.06.022 |
| Popis: | The article provides the results of applying machine learning methods for prediction of unfavorable outcome of the public procurement procedure in the building and grounds maintenance domain. Based on a comprehensive analysis of the domain it was decided to investigate the following risks: the risk of collusion among suppliers; the risk of conspiracy between customers and suppliers; the risk associated with inaccurate data in the Unified Information System. Usage of various classification techniques has been researched while modeling the problem in the domain. In order to form sustainable groups of suppliers, the association rule mining was done using the "Apriori" algorithm. While searching for representative characteristics of the groups of similar objects, the solution to the clustering problem was found using the Ward and K-means++ methods. The Cluster models, which were defined to analyze each of the collusion risks, were built on the feature space. The models make it possible to identify the most typical behavioral patterns of two suppliers with each other as well as the customer with the supplier. |
| Databáze: | OpenAIRE |
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