Legal Judgment Prediction in the Context of Energy Market using Gradient Boosting

被引：0

作者：

Franca, Joao V. F. ^{[1
]}

Boaro, Jose M. C. ^{[1
]}

dos Santos, Pedro T. C. ^{[1
]}

Henrique, Fernando ^{[1
]}

Garcia, Venicius ^{[1
]}

Manfredini, Caio ^{[1
]}

Junior, Domingos A. D. ^{[1
]}

de Oliveira, Francisco Y. C. ^{[1
]}

Castro, Carlos E. P. ^{[1
]}

Braz Junior, Geraldo ^{[1
]}

Silva, Aristofanes C. ^{[1
]}

Paiva, Anselmo C. ^{[1
]}

de Oliveira, Milton S. L. ^{[2
]}

Moreira e Moraes, Renato U. ^{[2
]}

Alves, Erika W. B. A. L. ^{[2
]}

Sobral Neto, Jose S. ^{[2
]}

机构：

[1] Fed Univ Maranhao UFMA, Appl Comp Grp NCA, Sao Luis, Maranhao, Brazil

[2] Equatorial Energy SA, Sao Luis, MA, Brazil

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2020年

关键词：

Legal Judgment Prediction; Machine Learning; XGBoost; TPE; SMOTE;

D O I：

10.1109/smc42975.2020.9283297

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A recurring problem for energy supply companies is the guarantee of the quality of services, which is regulated in many cases. Even so, there are many lawsuits against energy distribution companies, for several reasons, increasing the operating costs of these companies, in many situations with cases that could be resolved via negotiation. This work proposes a method to predict legal judgment outcome regarding the chance of being won or lost by the company. The idea is to understand in which lawsuits more effort should be made to negotiate with the court. The methodology is divided into five stages: feature extraction, sampling with Borderline SMOTE, feature encoding with Target Encoding, classification with XGBoost, and evaluation. The proposed method was evaluated in a database with more than seventy thousand lawsuits, with different outcomes and types, reaching an accuracy of 78.13%, F1 of 74.34%, and AUC of 77.59%.

引用

页码：875 / 880

页数：6

共 50 条

[31] Gradient Boosting and LSTM Based Hybrid Ensemble Learning for Two Step Prediction of Stock Market
Mohapatra, Pratyush Ranjan
Parida, Ajaya Kumar
Swain, Santosh Kumar
Basa, Santi Swarup
JOURNAL OF ADVANCES IN INFORMATION TECHNOLOGY, 2023, 14 (06) : 1254 - 1260
[32] Legal Judgment Prediction for Canadian Appeal Cases
Almuslim, Intisar
Inkpen, Diana
2022 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND MACHINE LEARNING APPLICATIONS (CDMA 2022), 2022, : 163 - 168
[33] Predicting Student Employability Through the Internship Context Using Gradient Boosting Models
Saidani, Oumaima
Menzli, Leila Jamel
Ksibi, Amel
Alturki, Nazik
Alluhaidan, Ala Saleh
IEEE ACCESS, 2022, 10 : 46472 - 46489
[34] Multiclass Legal Judgment Outcome Prediction for Consumer Lawsuits using XGBoost and TPE
dos Santos, Pedro T. C.
Henrique, Fernando
Garcia, Venicius
Ferreira, Victor R. S.
dos Santos Neto, Antonino C.
Souza, Johnatan C.
Manfredini, Caio
Franca, Joao V. F.
Boaro, Jose M. C.
Braz Junior, Geraldo
Silva, Aristofanes C.
Paiva, Anselmo C.
de Oliveira, Milton S. L.
Alves, Erika W. B. A. L.
Moreira e Moraes, Renato U.
Sobral Neto, Jose S.
2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2020, : 881 - 886
[35] Feature selection using ModifiedBoostARoota and prediction of heart diseases using Gradient Boosting algorithms
Anuradha, P.
David, Vasantha Kalyani
2021 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION, AND INTELLIGENT SYSTEMS (ICCCIS), 2021, : 19 - 23
[36] NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Duan, Tony
Avati, Anand
Ding, Daisy Yi
Thai, Khanh K.
Basu, Sanjay
Ng, Andrew
Schuler, Alejandro
25TH AMERICAS CONFERENCE ON INFORMATION SYSTEMS (AMCIS 2019), 2019,
[37] NGBoost: Natural Gradient Boosting for Probabilistic Prediction
Duan, Tony
Avati, Anand
Ding, Daisy Yi
Thai, Khanh K.
Basu, Sanjay
Ng, Andrew
Schuler, Alejandro
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119, 2020, 119
[38] Prediction of Cardiotoxicity for Breast Cancer Patients Using Light Gradient Boosting Machine
Jiang, Z.
Diao, P.
Liang, Y.
Dai, K.
Li, H.
Wang, H.
Chen, Y.
Lu, M.
Kuang, Y.
MEDICAL PHYSICS, 2021, 48 (06)
[39] Prediction of Mean Wave Overtopping Discharge Using Gradient Boosting Decision Trees
den Bieman, Joost P.
Wilms, Josefine M.
van den Boogaard, Henk F. P.
van Gent, Marcel R. A.
WATER, 2020, 12 (06)
[40] Freight Vehicle Travel Time Prediction Using Gradient Boosting Regression Tree
Li, Xia
Bai, Ruibin
2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016), 2016, : 1010 - 1015

← 1 2 3 4 5 →