Journal of Data Mining in Genomics & Proteomics

Benchmarking regression algorithms for income prediction modeling

2^nd International Conference on Big Data Analysis and Data Mining

November 30-December 01, 2015 San Antonio, USA

Azamat Kibekbaev and Ekrem Duma

Ozyegin University, Turkey

Posters-Accepted Abstracts: J Data Mining In Genomics & Proteomics

Abstract:

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. The experiments also indicated that many regression techniques yield performances which are quite competitive with each other (as MARS, M5P, ANN and LSSVM).

Biography :

Azamat Kibekbaev received his B.S. (2011) from Fatih University and M.S. (2013) from same University both in Industrial Engineering, and doing his Ph.D. (2013) in Industrial Engineering at ÖzyeÄ?in University. He is particularly interested in data mining applications in banking and healthcare analytics.

Email: kibekbaev.azamat@ozu.edu.tr