Exploring filter-based feature selection techniques for software quality classification Online publication date: Sat, 09-Aug-2014
by Kehan Gao; Taghi M. Khoshgoftaar; Huanjing Wang
International Journal of Information and Decision Sciences (IJIDS), Vol. 4, No. 2/3, 2012
Abstract: The quality of software products can be estimated and improved by building software quality classification models. The predictive accuracy of the software quality classification models is usually affected by two factors: the learning model(s) used in classification and the quality of the data. This study examined both influencing factors, but we concentrated more on the quality of the data by selecting a subset of relevant features for building classification models. We investigated four filter-based feature selection techniques in a case study on a very large telecommunications software system. The empirical results demonstrated that by applying attribute selection we can build classification models with prediction accuracy comparable to or even better than those built with a complete set of attributes, even though the smaller subset of attributes had less than 15% of the complete set of attributes.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Decision Sciences (IJIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook