Enhanced framework for ensemble effort estimation by using recursive‐based classification

Azath, H.; Raja, M.; Pandimurugan, V.; Krishnan, Sangeetha; Rajendran, Lakshminarayanan

doi:10.1049/sfw2.12020

Cited by 9 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, proposed MoWE model is compared with other heterogenous ensemble methods found in literature, including gradient boosting [ 75 ] stacking [ 76 ] majority voting and weighted ensemble (with non-optimized weights) [ 30 ]. Further, performance comparison of our proposed MoMdbWE techniques is made with previous EEE studies ( Table 11 ).…”

Section: Methodsmentioning

confidence: 99%

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

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

View full text Add to dashboard Cite

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 accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Yasmin,

Haider Butt,

Daud

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…An ensemble framework is presented for effort estimation using ML algorithms to obtain better accuracy estimates for error measurements. 18 The framework, which is based on an enhanced RF algorithm, succeeded in this task when it was compared to existing effort estimation methods.…”

Section: Ensemble Effort Estimation Methodsmentioning

confidence: 99%

“…However, these measurements can be biased. 8,26,85,86 Therefore, to evaluate the proposed estimation method, in this study, alternative criteria are used that provide an unbiased symmetric distribution as follows: SSE (Equation [18]), MAE (Equation [19]), RMSE (Equation [20]), MBRE (Equation [21]), MIBRE (Equation [22]), MdMRE (Equation [23]), and PRED(x) (where x = 0.25 in this study; Equation [24]). All of these criteria have been proven to be effective.…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…The estimation results of the three basic models are combined into an ensemble using combination rules (mean, median, and inverse rank‐weighted mean). An ensemble framework is presented for effort estimation using ML algorithms to obtain better accuracy estimates for error measurements 18 . The framework, which is based on an enhanced RF algorithm, succeeded in this task when it was compared to existing effort estimation methods.…”

Section: Related Workmentioning

confidence: 99%

“…Given the complexity of software development projects today, effort estimation requires ML assistance 12 . Therefore, based on their review, we summarize 21 selected recent studies 13–40 over the past 7 years (2016–2021) on software effort estimation using statistical and ML techniques or just ML techniques. Specifically, seven statistical and ML techniques, namely, multilayer perceptron (MLP), support vector regression (SVR), decision tree (DT), random forest (RF), multiple linear regression (MLR), K ‐nearest neighbor (KNN), and gradient boosting (GB), were discovered to have been most frequently used in SDEE at 16%, 13%, 13%, 11%, 9%, 7%, and 4% of studies, respectively (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Nhung,

Van Hai,

Silhavy

et al. 2023

J Software Evolu Process

View full text Add to dashboard Cite

Accurate effort estimation is necessary for efficient management of software development projects, as it relates to human resource management. Ensemble methods, which employ multiple statistical and machine learning techniques, are more robust, reliable, and accurate effort estimation techniques. This study develops a stacking ensemble model based on optimization correction factors by integrating seven statistical and machine learning techniques (K‐nearest neighbor, random forest, support vector regression, multilayer perception, gradient boosting, linear regression, and decision tree). The grid search optimization method is used to obtain valid search ranges and optimal configuration values, allowing more accurate estimation. We conducted experiments to compare the proposed method with related methods, such as use case points‐based single methods, optimization correction factors‐based single methods, and ensemble methods. The estimation accuracies of the methods were evaluated using statistical tests and unbiased performance measures on a total of four datasets, thus demonstrating the effectiveness of the proposed method more clearly. The proposed method successfully maintained its estimation accuracy across the four experimental datasets and gave the best results in terms of the sum of squares errors, mean absolute error, root mean square error, mean balance relative error, mean inverted balance relative error, median of magnitude of relative error, and percentage of prediction (0.25). The p‐value for the t‐test showed that the proposed method is statistically superior to other methods in terms of estimation accuracy. The results show that the proposed method is a comprehensive approach for improving estimation accuracy and minimizing project risks in the early stages of software development.

show abstract

Cost Adjustment for Software Crowdsourcing Tasks Using Ensemble Effort Estimation and Topic Modeling

Yasmin

2024

Arab J Sci Eng

View full text Add to dashboard Cite

Enhanced framework for ensemble effort estimation by using recursive‐based classification

Cited by 9 publications

References 22 publications

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

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

Incorporating statistical and machine learning techniques into the optimization of correction factors for software development effort estimation

Cost Adjustment for Software Crowdsourcing Tasks Using Ensemble Effort Estimation and Topic Modeling

Contact Info

Product

Resources

About