Identifying IT Purchases Anomalies in the Brazilian Government Procurement System Using Deep Learning

Domingos, Silvio L.; Carvalho, Rommel N.; Carvalho, Ricardo Silva; Ramos, Guilherme

doi:10.1109/icmla.2016.0129

Cited by 18 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[25]. At present it has been widely applied to a variety of fields, including government policy [27] [40], and so on, and financial technology (FinTech) [41] is also one of popular applications. In addition, the series of decision tree models usually perform well in processing mid-size data, such as classification and regression trees (CART) [42], ID3 [43], and C4.5 [44].…”

Section: B Machine Learning Application In Decision Makingmentioning

confidence: 99%

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

et al. 2020

View full text Add to dashboard Cite

In accordance with the statistical analysis, the industrial performance is usually related to research and development (R&D) intensity, and this factor indeed plausibly brings the biggest profit with patents and supporting products to the development of semiconductor industry. How to evaluate the completive performance of modern industries is an increasing issue, especially for the semiconductor industries in these decades. However, almost every traditional statistical model is deterred by the hypothesis of population and independent correlation among each feature, and this makes the result of typical regression model potentially lose reliability. To avoid this weakness, this paper therefore applies a gradient boosting based method-XGBoost to evaluate the feature importance of semiconductor industries. In the simulation experiments, different findings revel certain information, apart from R&D intensity, actually sway the gross net value in the annual financial announcement of semiconductor industries. Moreover, this paper proposes another concept to evaluate the essential factor contributing the development of semiconductor industries. Instead of only focusing on the effect of R&D intensity, this paper also predicts the future growth rate (GR) of net value by applying the greedy search of XGBoost Regression. INDEX TERMS XGBoost, Boosting Regression, Semiconductor Industry I. INOTRODUCTION Research and development (R&D) management is one of important issues in the research field of human resource management (HRM), and how to make proper investment in the expense of R&D management has become an increasing issue. The most works reveal a common conclusion in the literature that R&D has a positive effect on the performance of industries, so the term "R&D Intensity" is primarily applied in the analysis of relevant works and defined as the

show abstract

Section: B Machine Learning Application In Decision Makingmentioning

confidence: 99%

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Apesar de serem entidades governamentais, os órgãos de controladoria, como todas as empresas, trabalham com recurso limitado para suas investigações. Com isso, existe a necessidade de priorizar as investigações, estimando o risco dos contratos, de modo a investigar primeiramente os contratos mais suspeitos [Silvio L. Domingos and Ramos 2016].…”

Section: Estimando Risco Em Contratos Públicosunclassified

“…Estimativas de risco têm sido utilizadas extensivamente em outras áreas, como análise de crédito, onde os pesquisadores dessa área obtiveram resultados promissores aplicando diferentes métodos estatísticos e de Inteligência Artificial (IA), como aprendizagem de máquina (AM) [Zan Huang 2004]. A estimativa de risco dos contratos pode ser feito de forma manual, mas com o avanço da tecnologia, os órgãos de controladoria precisaram se modernizar e utilizar ferramentas que a tecnologia da informação proporciona, as quais são capazes de detectar padrões sobre as contratações irregulares ou ilícitas [Silvio L. Domingos and Ramos 2016].…”

Section: Estimando Risco Em Contratos Públicosunclassified

“…Os dados usados nos experimentos foram criados a partir de dados utilizados por órgãos de controle externo estaduais e federais fornecidos aos autores e que descrevemos a seguir. Assim como em outros trabalhos da área [Sun and Sales 2018;Silvio L. Domingos and Ramos 2016;Gomes et al 2017], não descrevemos em detalhes todas as características usadas pelos órgãos de controle para fiscalizar empresas e contratos. Os órgãos entendem que o sigilo da definição das características é estratégico.…”

Section: Dados Usadosunclassified

“…No contexto da gestão pública federal brasileira, há interesse e estudos sobre a eficácia do uso de aprendizagem de máquina para estimativa de risco de contratos [Sun and Sales 2018;Silvio L. Domingos and Ramos 2016]. Assim como na análise de risco de crédito e predição de risco criminal em geral, injustiça na estimativa de risco para contratos públicos pode também causar grande prejuízo à vida de pessoas.…”

Section: Introductionunclassified

See 2 more Smart Citations

Fairness in Risk Estimation of Brazilian Public Contracts

Lima¹,

Andrade²

2019

Anais Do VII Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2019)

View full text Add to dashboard Cite

Brazilian government agencies are currently using machine learning models to make public contracts audition through risk estimation. Recent works have shown that decision making models, like risk estimation, may be unfair. Despite the fact that risk estimations of public contracts may be unfair, no studies evaluating model fairness have been found. This work contributes by analysing fairness over risk estimation of brazilian public contract. This article found that currently used models are unfair and biased towards a specific class. This means that people within this class may be negatively affected by these decision making models unfairness through risk estimation of their companies.

show abstract

Implementation and Analysis of Contextual Neural Networks in H2O Framework

Wołk

Burnell

2019

Intelligent Information and Database Systems

View full text Add to dashboard Cite

Identifying IT Purchases Anomalies in the Brazilian Government Procurement System Using Deep Learning

Cited by 18 publications

References 16 publications

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

A Boosting Regression-Based Method to Evaluate the Vital Essence in Semiconductor Industry Performance

Fairness in Risk Estimation of Brazilian Public Contracts

Implementation and Analysis of Contextual Neural Networks in H2O Framework

Contact Info

Product

Resources

About