Bayesian Hyperparameter Optimization and Ensemble Learning for Machine Learning Models on Software Effort Estimation

Marco, Robert; Ahmad, Sakinah Sharifah Syed; Ahmad, Sabrina

doi:10.14569/ijacsa.2022.0130351

Cited by 13 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marco et al [ 53 ] tried to use AdaBoost ensemble learning method and RFR for different data sets. Several machine learning algorithms, for different data sets MLP, SVR, CART, kNN, and RFR were applied.…”

Section: Discussionmentioning

confidence: 99%

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

et al. 2023

View full text Add to dashboard Cite

The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Variational autoencoders (VAEs) are generative models that learn to simulate the latent representation of data [37]. It is used to improve the quality of the generated outputs [38].…”

Section: Phase 3: Build the Variational Autoencoder (Vae) Modelmentioning

confidence: 99%

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

2024

IJIES

View full text Add to dashboard Cite

Traceability link recovery is a crucial task in software engineering that ensures the development of dependable and credible software systems. Traceability links between requirements and source code support various activities in the software development process, including change management and software maintenance. These links can be established manually or automatically. Manual trace retrieval is a time-consuming task. Automatic trace retrieval can be performed via various tools such as information retrieval or machine learning methods. Some automatic tools couldn't retrieve the links between requirements and source code. Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced. This study proposes an approach that addresses the low precision problem caused by the term mismatch problem between requirements and source code to obtain the greatest improvements in trace retrieval accuracy. The proposed approach utilizes a variational autoencoder (VAE), an unsupervised deep-learning model in the automated trace retrieval process. We have conducted a series of experiments on three datasets: eTour, SMOS, and eANCI to evaluate our approach against existing approaches. In order to validate the effectiveness of our proposed approach, we compared it to three previous studies that addressed the same problem and utilized the same datasets: the first study used unsupervised machine learning based on clustering, the second study used active learning, and the third study used a classification machine learning. The results show that our proposed approach improves the trace retrieval precision in the automated trace retrieval process.

show abstract

“…Ensembling is obtained by combining various models. Bagging, boosting, voting, etc., are some of the ensemble approaches [35]. Here we aggregated predictions of various models, i.e., averaged the output predictions of all models and produced one model closer to the actual effort than any individual model.…”

Section: E Votingmentioning

confidence: 99%

Software Effort Estimation through Ensembling of Base Models in Machine Learning using a Voting Estimator

Kumar¹,

Bilgaiyan²,

Mishra³

2023

IJACSA

View full text Add to dashboard Cite

For a long time, researchers have been working to predict the effort of software development with the help of various machine learning algorithms. These algorithms are known for better understanding the underlying facts inside the data and improving the prediction rate than conventional approaches such as line of code and functional point approaches. According to no free lunch theory, there is no single algorithm which gives better predictions on all the datasets. To remove this bias our work aims to provide a better model for software effort estimation and thereby reduce the distance between the actual and predicted effort for future projects. The authors proposed an ensembling of regressor models using voting estimator for better predictions to reduce the error rate to over the biasness provide by single machine learning algorithm. The results obtained show that the ensemble models were better than those from the single models used on different datasets.

show abstract

Bayesian Hyperparameter Optimization and Ensemble Learning for Machine Learning Models on Software Effort Estimation

Cited by 13 publications

References 0 publications

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

Software Effort Estimation through Ensembling of Base Models in Machine Learning using a Voting Estimator

Contact Info

Product

Resources

About