Measuring the Risk of Public Contracts Using Bayesian Classifiers
Abstract
Bayesian classifiers are widely used in machine learning supervised models where there is a reasonable reliability in the dependent variable. This work aims to create a risk measurement model of companies that negotiate with the government using indicators grouped into four risk dimensions: operational capacity, history of penalties and findings, bidding profile, and political ties. It is expected that this model contributes to the selection of contracts to be audited under the central unit of internal control of the Brazilian government, responsible for auditing more than 30,000 public contracts per year.
Cite
Text
Sales and Carvalho. "Measuring the Risk of Public Contracts Using Bayesian Classifiers." Conference on Uncertainty in Artificial Intelligence, 2016.Markdown
[Sales and Carvalho. "Measuring the Risk of Public Contracts Using Bayesian Classifiers." Conference on Uncertainty in Artificial Intelligence, 2016.](https://mlanthology.org/uai/2016/sales2016uai-measuring/)BibTeX
@inproceedings{sales2016uai-measuring,
title = {{Measuring the Risk of Public Contracts Using Bayesian Classifiers}},
author = {Sales, Leonardo Jorge and Carvalho, Rommel Novaes},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2016},
pages = {7-13},
url = {https://mlanthology.org/uai/2016/sales2016uai-measuring/}
}