Software effort estimation using machine learning methods

2007 ◽  
Author(s):  
Bilge Baskeles ◽  
Burak Turhan ◽  
Ayse Bener
Keyword(s):  
Machine Learning ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Optimizing SVR using Local Best PSO for Software Effort Estimation

2016 ◽  
Vol 1 (1) ◽  
pp. 28 ◽  
Author(s):  
Dinda Novitasari ◽  
Imam Cholissodin ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Software Industry ◽  
Optimal Parameter ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Proposed Model

Abstract. In the software industry world, it’s known to fulfill the tremendous demand. Therefore, estimating effort is needed to optimize the accuracy of the results, because it has the weakness in the personal analysis of experts who tend to be less objective. SVR is one of clever algorithm as machine learning methods that can be used. There are two problems when applying it; select features and find optimal parameter value. This paper proposed local best PSO-SVR to solve the problem. The result of experiment showed that the proposed model outperforms PSO-SVR and T-SVR in accuracy. Keywords: Optimization, SVR, Optimal Parameter, Feature Selection, Local Best PSO, Software Effort Estimation

Sign in / Sign up

Export Citation Format

Share Document