Prediction of unregistered power consumption lawsuits and its correlated factors based on customer data using extreme gradient boosting model

Oliveira, Francisco Y. C. de; Silva, Aristófanes Corrêa; Alves, Erika W. B. A. L.; Oliveira, Milton S. L. de; Pinheiro, Lucas; Cutrim, Pedro Thiago; Diniz, João Otávio Bandeira; Silva, Giovanni L. F.; Quintanilha, Darlan B. P.; Neto, Otilio Silva; Fernandes, Vandécia Rejane Monteiro; Bráz, Geraldo; Cavalcante, André Borges

doi:10.1109/smc.2019.8914366

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2019

DOI: 10.1109/smc.2019.8914366

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Prediction of unregistered power consumption lawsuits and its correlated factors based on customer data using extreme gradient boosting model

Francisco Y. C. de Oliveira

Aristófanes Corrêa Silva

Erika W. B. A. L. Alves³

et al.

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Cited by 3 publications

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Chen

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et al. 2020

2020 IEEE 5th International Symposium on Smart and Wireless Systems Within the Conferences on Intelligent Data Acquisition and

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Prediction of unregistered power consumption lawsuits and its correlated factors based on customer data using extreme gradient boosting model

Cited by 3 publications

References 20 publications

An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction

An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction

Multiclass Legal Judgment Outcome Prediction for Consumer Lawsuits using XGBoost and TPE

Deployment of a Machine Learning System for Predicting Lawsuits Against Power Companies: Lessons Learned from an Agile Testing Experience for Improving Software Quality

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