A Novel Class-Imbalance Learning Approach for Both Within-Project and Cross-Project Defect Prediction

Gong, Lina; Jiang, Shujuan; Bo, Lili; Jiang, Li; Qian, Junyan

doi:10.1109/tr.2019.2895462

Cited by 58 publications

(22 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, the class overlap issue has a performance impact on prediction; it also shows that when removing the class overlap instance, strategies such as oversampling should be considered to solve the class imbalance problem. This strategy can be used in other application domains, such as software defect predictions, to solve the class imbalance problem [33]. Also, for cross-project software defect prediction, the class imbalance problem can be solved using this SNCR strategy [34] [35].…”

Section: Discussionmentioning

confidence: 99%

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Important physiological information is hidden in electroencephalography (EEG), which can reflect the human brain's activity. EEG, which is a kind of complicated signal, can be used for epileptic seizure detection and epilepsy diagnosis via machine learning. A large amount of effort, including raw signal preprocessing and data preprocessing for machine learning, is required for constructing high-quality training datasets because the classification performance highly depends on high-quality data. Feature extraction has been widely used in EEG-based early epileptic seizure detection. Due to the complexity of data collection and labeling, some of the training instances are inevitably mislabeled. That means some similar instances have different labels. This is called the issue of class overlap, which leads to a poor class boundary for classification models and makes constructing a high-quality classification model more difficult. However, the previous studies investigating the impact of the class overlap for EEG data is quite limited. Our goal is to investigate the impact of the class overlap on EEG-based early epileptic seizure detection. We propose a special neighborhood cleaning rule (SNCR) to solve the class overlap issue. To alleviate the class overlap issue, we conduct large-scale experiments on two widely-used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy, i.e., the improved k-means clustering cleaning approach (IKMCCA). The experimental results show that the classification model can achieve significantly better performance in terms of AUC, recall, and F1 metrics when using our proposed SNCR strategy. Therefore, for EEG-based early epileptic seizure detection, we recommend researchers to apply the SNCR strategy to mitigate the class overlap issue and use the SNCR strategy to perform data preprocessing in a future related study.

show abstract

Section: Discussionmentioning

confidence: 99%

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Chen

Li³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The sampling method adds or removes data instances to achieve a balanced training set. It might result in the loss of much important information, which can lead to unsatisfactory results [79, 80]. After analysing our empirical evidence, we found SMOTE produces optimal results better compared to these sampling approaches.…”

Section: Empirical Study and The Proposed Approachmentioning

confidence: 93%

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

2020

View full text Add to dashboard Cite

Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

show abstract

“…Besides homogeneous defect prediction, heterogeneous defect prediction has recently become a great process. Gong et al [47] utilized the thought of stratification embedded in the nearest neighbor to produce evolving training datasets with balanced data. Zou et al [48] proposed a method named Joint Feature representation with double marginalized denoising auto-encoders to learn the global and local features, and they introduced local data gravitation between source and target domains to determine instance weight in the learning process (Zou et al [49]).…”

Section: Related Workmentioning

confidence: 99%

An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

Ren

Peng

Zheng

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

Homogeneous cross-project defect prediction (HCPDP) aims to apply a binary classification model built on source projects to a target project with the same metrics. However, there is still room for improvement in the performance of the existing HCPDP models. This study has proposed a novel approach, including one-to-one and many-to-one predictions. First, we apply the Jensen-Shannon divergence to select the most similar source project automatically. Second, relative density estimation is introduced to choose the suitable instance of the selected source project. Third, one-to-one and many-to-one prediction models are trained by the selected instances. Finally, two benchmark datasets are used to evaluate the proposed approach. Compared to the state-of-the-art methods, the experimental results demonstrated that the proposed approach could improve the prediction performance in the F1-score, AUC, and G-mean metrics and exhibit strong adaptability to the traditional classifiers.

show abstract

A Novel Class-Imbalance Learning Approach for Both Within-Project and Cross-Project Defect Prediction

Cited by 58 publications

References 38 publications

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

An Approach to Improving Homogeneous Cross-Project Defect Prediction by Jensen-Shannon Divergence and Relative Density

Contact Info

Product

Resources

About