Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Profit Maximizing Logistic Regression Modeling for Credit Scoring

Abstract: 

Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model. Therefore, in this work we propose a method which optimizes the coefficients of a logistic regression model using a genetic algorithm. The proposed implemented technique shows a significant improvement compared to regular maximum likelihood based logistic regression models on real-life data sets in terms of total profit, which is the ultimate goal for most businesses.

up
1 user has voted: Arnout Devos

Paper Details

Authors:
Arnout Devos
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted On:
30 May 2018 - 8:30pm
Short Link:
Type:
Poster
Event:
Data Science Workshop 2018
Presenter's Name:
Arnout Devos
Paper Code:
1024
Document Year:
2018
Cite

Document Files

PosterA1_Arnout_Devos_DSW2018.pdf

(169)

Keywords

Subscribe

Send Author a Private Message
[1] Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, "Profit Maximizing Logistic Regression Modeling for Credit Scoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3221. Accessed: Dec. 08, 2019.
@article{3221-18,
url = {http://sigport.org/3221},
author = {Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme },
publisher = {IEEE SigPort},
title = {Profit Maximizing Logistic Regression Modeling for Credit Scoring},
year = {2018} }
TY - EJOUR
T1 - Profit Maximizing Logistic Regression Modeling for Credit Scoring
AU - Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3221
ER -
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. IEEE SigPort. http://sigport.org/3221
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, 2018. Profit Maximizing Logistic Regression Modeling for Credit Scoring. Available at: http://sigport.org/3221.
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). "Profit Maximizing Logistic Regression Modeling for Credit Scoring." Web.
1. Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. Profit Maximizing Logistic Regression Modeling for Credit Scoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3221