Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy.

ABSTRACT: Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables ac

SUBMITTER: Yasmin A 

PROVIDER: S-EPMC10994292 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature