Evaluation of feature extraction methods on software cost estimation

Burak Turhan, Onur Kutlubay, Ayse Bener

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use Principal Component Analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods with support vector regression (SVR) on publicly available datasets. Our results for these datasets indicate there is no significant difference between the performances of these linear and non-linear feature extraction methods.

Original languageEnglish
Title of host publicationProceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
Number of pages1
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 - Madrid, Spain
Duration: 20 Sep 200721 Sep 2007

Conference

Conference1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007
CountrySpain
CityMadrid
Period20/09/0721/09/07

Cite this

Turhan, B., Kutlubay, O., & Bener, A. (2007). Evaluation of feature extraction methods on software cost estimation. In Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007 [4343793] https://doi.org/10.1109/ESEM.2007.45