Authors :
Beatrice O. Akumba; Iorshase Agaji; Nachamada Blamah; Emmanuel Ogalla
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/Ai0T
DOI :
https://doi.org/10.5281/zenodo.8017130
Abstract :
This paper introduced the concept of a hybrid
machine learning method for estimating software project
cost. The literature review of some of the models
commonly used in the software project cost estimation
was carried out. A hybrid method of algorithms
comprising Random Forest (RF), Kalman Filter (KF)
and Support Vector Machine (SVM) algorithms
respectively were proposed to predict the software
project cost and its completion time for software
projects. The proposed architecture of the model was
presented as well as the proposed the model.
Keywords :
Software Cost Estimation, Machine Learning, Cost Estimation Models, Kalman Filter Algorithm, Support Vector Machine, Random Forest.
This paper introduced the concept of a hybrid
machine learning method for estimating software project
cost. The literature review of some of the models
commonly used in the software project cost estimation
was carried out. A hybrid method of algorithms
comprising Random Forest (RF), Kalman Filter (KF)
and Support Vector Machine (SVM) algorithms
respectively were proposed to predict the software
project cost and its completion time for software
projects. The proposed architecture of the model was
presented as well as the proposed the model.
Keywords :
Software Cost Estimation, Machine Learning, Cost Estimation Models, Kalman Filter Algorithm, Support Vector Machine, Random Forest.
