GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation

2010 ◽  
Vol 52 (11) ◽  
pp. 1155-1166 ◽  
Author(s):  
Adriano L.I. Oliveira ◽  
Petronio L. Braga ◽  
Ricardo M.F. Lima ◽  
Márcio L. Cornélio
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Parameters Optimization ◽  
Effort Estimation ◽  
Software Effort Estimation

scholarly journals COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION

2021 ◽  
Vol 6 (2) ◽  
pp. 167-174
Author(s):  
Abdul Latif ◽  
Lady Agustin Fitriana ◽  
Muhammad Rifqi Firdaus
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Linear Regression ◽  
Software Development ◽  
Data Mining Algorithm ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Data Mining Technique ◽  
Software Business

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection

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

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