.png){kind=icon}
返回
Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2025.3544625.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In Software Quality Assurance (SQA), predicting defect-prone software modules is essential for ensuring software reliability and consistency. This task is commonly achieved through Machine Learning (ML) techniques, but improving model performance typically incurs significant computational costs. These high computational costs and uncertain payoffs make most Software engineering researchers reluctant to optimize ML models. This creates a need for novel techniques that can achieve near-optimal performance of hyperparameter settings while maintaining the computational efficiency of default settings. To address this, we employed five ML models, Decision Tree, Ranger, Random Forest, Support Vector Machine, and k-nearest Neighbors, and optimized their parameters using the random search technique. Our experiments covered six diverse Software Fault Prediction (SFP) datasets, encompassing various software features, application domains, and defect patterns, to evaluate the approach's generalizability and effectiveness. Moreover, the Permutation Feature Importance (PFI)-based model-agnostic method was employed to identify the top ten features most critical for model accuracy and efficiency. These selected features were used to retrain the ML models without hyperparameters (default settings) to determine whether similar performance could be achieved at low computational cost. The results show an average accuracy improvement of 77.39% and a 92.02% reduction in computational cost. The most important case attained a 99.25% accuracy improvement and a 96.77% cost reduction. Such results clearly show that PFI-based feature selection is capable of high performance at a fraction of computational cost, offering an efficient solution for software engineers to optimize ML models.
Keyword:
Computational modeling
Feature extraction
Accuracy
Computational efficiency
Predictive models
Optimization
Support vector machines
Random forests
Radio frequency
Decision trees
Model-agnostic techniques
permutation feature importance (PFI)
software fault prediction (SFP)
predictive accuracy
machine learning (ML)
computational cost
default settings
hyperparameter
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
BRAF V600E Gene Mutation in Colonic Adenocarcinomas. Immunohistochemical Detection Using Tissue Microarray and Clinicopathologic Characteristics结肠腺癌中BRAF V600E基因突变。组织芯片免疫组织化学检测与临床病理特征
PREAI
分享收藏Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better高效深度学习: 关于使深度学习模型更小、更快、更好的调查OAAI
OAAI分享收藏Intercellular Adhesion Molecule 1 (ICAM-1) Gene Variant is Associated with Coronary Artery Calcification Independent of Soluble ICAM-1 Levels细胞间粘附分子1 (ICAM-1) 基因变异与冠状动脉钙化相关，与可溶性ICAM-1水平无关
PREAI
分享收藏Diversity based imbalance learning approach for software fault prediction using machine learning models基于机器学习模型的软件故障预测的多样性不平衡学习方法
PREAI
分享收藏Hybrid YSGOA and neural networks based software failure prediction in cloud systems.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏分享收藏
查看更多内容