Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation

Villalobos-Arias, Leonardo; Quesada-López, Christian; Guevara-Coto, José; Martinez, Alejandra; Jenkins, Marcelo

doi:10.1145/3416508.3417121

Cited by 24 publications

(14 citation statements)

References 36 publications

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“…1) Support Vector Regression (SVR) SVR has been widely studied by researchers in the area of SDEE [24], [50], [51]. It is machine learning technique which works by mapping non-linear separable patterns in data into higher feature space with an aim of minimizing the loss function along with maximizing support vector bounds.…”

Section: Effort Estimation Methodsmentioning

confidence: 99%

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Gautam

Singh

2022

IEEE Access

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The software engineering researchers have worked on different dimensions to facilitate better software effort estimates, including those focusing on dataset quality improvement. In this research, we specially investigated the effectiveness of outlier removal to improve estimation performance of 5 machine learning (ML) methods (Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbor, and Gradient Boosting Machines) for software development effort estimation (SDEE). We propose a novel discretization method based on Golden Section (dubbed as Golden Section based Adaptive Discretization, GSAD) to identify optimal number of outliers for SDEE dataset. The results signify the importance of optimal number of outliers' removal to improve estimations. Moreover, the results obtained after applying GSAD technique have been compared with IQR and Cooks' distance based outlier identification methods over 4 datasets: ISBSG Release 2021, UCP, NASA93 and China. The empirical results confirm that the performance of ML based SDEE methods is generally improving by employing GSAD and the proposed GSAD method has the ability to compete with the other prevalent outlier identification methods.INDEX TERMS software development effort estimation, machine learning, discretization, outlier identification, golden section method.

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Section: Effort Estimation Methodsmentioning

confidence: 99%

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Gautam

Singh

2022

IEEE Access

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“…The COCOMO is still in practice; researchers and practitioners are extending and modifying it with different concepts to introduce new effort estimation techniques as mentioned in these papers [14][15][16][17]. All the papers are published in IEEE publisher.…”

Section: Cocomo Imentioning

confidence: 99%

Prediction based cost estimation technique in agile development

Butt

Ercan²,

Binsawad³

et al. 2023

Advances in Engineering Software

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“…Most of the previous work in hyperparameter tuning tends to focus only on Grid Search and Random Search, or a comparison between them [9][10][11][12][13]. A comprehensive study on tunability is given in [14]; the authors constructed the tuning problem in statistical terms as well as suggesting tunability quantifying measures of algorithms' hyperparameters.…”

Section: Hyperparameter Tunningmentioning

confidence: 99%

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

et al. 2021

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Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

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Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation

Cited by 24 publications

References 36 publications

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Adaptive Discretization Using Golden Section to Aid Outlier Detection for Software Development Effort Estimation

Prediction based cost estimation technique in agile development

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

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