The Estimation of Ensemble Logistic Regression using Newton Raphson Parameter

Armin Lawi; Firman Aziz; Husna Gemasih; Mursalin Mursalin

Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January 2018, Aceh, Indonesia

Research Article

The Estimation of Ensemble Logistic Regression using Newton Raphson Parameter

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@INPROCEEDINGS{10.4108/eai.20-1-2018.2281940,
    author={Armin  Lawi and Firman  Aziz and Husna  Gemasih and Mursalin  Mursalin},
    title={The Estimation of Ensemble Logistic Regression using Newton Raphson Parameter},
    proceedings={Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January 2018, Aceh, Indonesia},
    publisher={EAI},
    proceedings_a={WMA-1},
    year={2019},
    month={9},
    keywords={credit scoring logistic regression ensemble bagging},
    doi={10.4108/eai.20-1-2018.2281940}
}

Armin Lawi
Firman Aziz
Husna Gemasih
Mursalin Mursalin
Year: 2019
The Estimation of Ensemble Logistic Regression using Newton Raphson Parameter
WMA-1
EAI
DOI: 10.4108/eai.20-1-2018.2281940

Armin Lawi¹^,*, Firman Aziz², Husna Gemasih³, Mursalin Mursalin⁴

1: Department of Computer Science, Hasanuddin University, Makassar, Indonesia
2: Post-Graduate Program of Electrical Engineering, Hasanuddin University, Makassar, Indonesia
3: Department of Informatics, Universitas Gajah Putih, Aceh, Indonesia
4: Department of Mathematics Education, Universitas Malikussaleh, Aceh Utara, Indonesia

*Contact email: armin@unhas.ac.id

Abstract

The large volume of customer data in the credit industry makes the development of an effective credit scoring model extremely important. The use of an ensemble model on statistical methods to solve credit scoring problems managed to get the best predictive performance. ensemble performance can still be improved by estimating the parameters using nonlinear equations. This paper proposes the estimation of ensemble Logistic Regression using Newton Raphson parameter. The results showed that proposed method successfully achieved the best performance by improving the performance of a single classification with an increase of 2% accuracy

Keywords: credit scoring logistic regression ensemble bagging

Published: 2019-09-20
Publisher: EAI

: http://dx.doi.org/10.4108/eai.20-1-2018.2281940