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Benchmarking regression algorithms for income prediction modeling

Title	Benchmarking regression algorithms for income prediction modeling
Author	Kibekbaev, Azamat, Duman, Ekrem
Publication Date:	2016
Publication Place	- Elsevier
Subject	Regulation, Income prediction, Regression techniques
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0306-4379
Record ID	d592fb15-4bc8-4b0b-b1ce-af6322626ba2
Library Location	Industrial Engineering
Date	2016
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.
DOI	10.1016/j.is.2016.05.001

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Özyeğin University

Benchmarking regression algorithms for income prediction modeling

Author Kibekbaev, Azamat, Duman, Ekrem

Publication Date 2016

Publication Place - Elsevier

Subject Regulation, Income prediction, Regression techniques

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0306-4379

Record ID d592fb15-4bc8-4b0b-b1ce-af6322626ba2

Library Location Industrial Engineering

Date 2016

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text This paper aims to predict incomes of customers for banks. In this large-scale income prediction benchmarking paper, we study the performance of various state-of-the-art regression algorithms (e.g. ordinary least squares regression, beta regression, robust regression, ridge regression, MARS, ANN, LS-SVM and CART, as well as two-stage models which combine multiple techniques) applied to five real-life datasets. A total of 16 techniques are compared using 10 different performance measures such as R2, hit rate and preciseness etc. It is found that the traditional linear regression results perform comparable to more sophisticated non-linear and two-stage models.

DOI 10.1016/j.is.2016.05.001