Please use this identifier to cite or link to this item:
Title: Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Authors: Li, Y.-F.
Xie, M. 
Goh, T.-N. 
Keywords: Machine learning
Ridge regression
Software cost estimation
Issue Date: Nov-2010
Citation: Li, Y.-F., Xie, M., Goh, T.-N. (2010-11). Adaptive ridge regression system for software cost estimating on multi-collinear datasets. Journal of Systems and Software 83 (11) : 2332-2343. ScholarBank@NUS Repository.
Abstract: Cost estimation is one of the most critical activities in software life cycle. In past decades, a number of techniques have been proposed for cost estimation. Linear regression is yet the most frequently applied method in the literature. However, a number of studies point out that linear regression is prone to low prediction accuracy. The low prediction accuracy is due to a number of reasons such as non-linearity and non-normality. One less addressed reason is the multi-collinearities which may lead to unstable regression coefficients. On the other hand, it has been reported that multi-collinearity spreads widely across the software engineering datasets. To tackle this problem and improve regression's accuracy, we propose a holistic problem-solving approach (named adaptive ridge regression system) integrating data transformation, multi-collinearity diagnosis, ridge regression technique and multi-objective optimization. The proposed system is tested on two real world datasets with the comparisons with OLS regression, stepwise regression and other machine learning methods. The results indicate that adaptive ridge regression system can significantly improve the performance of regressions on multi-collinear datasets and produce more explainable results than machine learning methods. © 2010 Elsevier Inc. All rights reserved.
Source Title: Journal of Systems and Software
ISSN: 01641212
DOI: 10.1016/j.jss.2010.07.032
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 8, 2018


checked on Oct 1, 2018

Page view(s)

checked on Sep 22, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.