Benchmarking regression algorithms for income prediction modeling

Name: Benchmarking regression algorithms for income prediction modeling
Author: Kibekbaev, Azamat, Duman, Ekrem

İsim	Benchmarking regression algorithms for income prediction modeling
Yazar	Kibekbaev, Azamat, Duman, Ekrem
Basım Tarihi:	2016
Basım Yeri	- Elsevier
Konu	Regulation, Income prediction, Regression techniques
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0306-4379
Kayıt Numarası	d592fb15-4bc8-4b0b-b1ce-af6322626ba2
Lokasyon	Industrial Engineering
Tarih	2016
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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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Benchmarking regression algorithms for income prediction modeling

Yazar Kibekbaev, Azamat, Duman, Ekrem

Basım Tarihi 2016

Basım Yeri - Elsevier

Konu Regulation, Income prediction, Regression techniques

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0306-4379

Kayıt Numarası d592fb15-4bc8-4b0b-b1ce-af6322626ba2

Lokasyon Industrial Engineering

Tarih 2016

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

Örnek Metin 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