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:	2015
Basım Yeri	- IEEE
Konu	Regulation, Income prediction, Regression techniques, Performance measures
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4673-9795-7
Kayıt Numarası	ecd7ca41-161a-426b-b535-4f536133fd28
Lokasyon	Industrial Engineering
Tarih	2015
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	10.1109/CSCI.2015.162

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Benchmarking regression algorithms for income prediction modeling

Yazar Kibekbaev, Azamat, Duman, Ekrem

Basım Tarihi 2015

Basım Yeri - IEEE

Konu Regulation, Income prediction, Regression techniques, Performance measures

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4673-9795-7

Kayıt Numarası ecd7ca41-161a-426b-b535-4f536133fd28

Lokasyon Industrial Engineering

Tarih 2015

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 10.1109/CSCI.2015.162