A GA-based Feature Selection and Parameters Optimization for Support Vector Regression Applied to Software Effort Estimation

被引：0
|
作者：
Braga, Petronio L. [1 ]
Oliveira, Adriano L. I. [1 ]
Meira, Silvio R. L. [2 ]
机构：
[1] Pernambuco State Univ, Dept Comp Syst, Recife, PE, Brazil
[2] Univ Fed Pernambuco, Ctr Informat, Recife, PE, Brazil
来源：
APPLIED COMPUTING 2008, VOLS 1-3 | 2008年
关键词：
Support Vector Regression; Feature Selection; Genetic Algorithm; Software effort estimation;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The precision of the estimation of the effort of software projects is very important for the competitiveness of software companies. Machine learning methods have recently been applied for this task, included methods based on sup port vector regression (SVR). This paper proposes and investigates the use of a genetic algorithm approach for simultaneously (1) select in optimal feature subset and (2) optimize SVR parameters, aiming to improve the precision of the software effort estimates. We report on experiments carried out using two datasets of software projects. In both datasets, the simulations have shown that the proposed GA-based approach was able to improve substantially the performance of SVR and outperform some recent results reported in the literature.
引用
收藏
页码：1788 / +
页数：3
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