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

Pandey, Sushant Kumar; Rathee, Deevashwer; Tripathi, Anil Kumar

doi:10.1049/iet-sen.2020.0119

Cited by 24 publications

(12 citation statements)

References 87 publications

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“…The receiver operating characteristic (ROC) curve ( 32) and the area under the curve (AUC) are introduced to provide a graphical plot and a quantitative value of measuring the proposed PSCNN model, respectively. ROC and AUC are obtained through the following two procedures: (i) ROC plot is firstly generated by charting the TP rate against the FP rate at different threshold degrees (33). (ii) AUC is then estimated by measuring the complete 2D area beneath the ROC curve from point (0, 0) to point (1, 1) (34).…”

Section: Measures and Explainabilitymentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

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Section: Measures and Explainabilitymentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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“…But kernel principal component analysis (kernel PCA) provides less computational cost. In the method, kernel PCA [16][17][18] is used for projecting feature vectors and it is performed on the extracted feature matrix. The following steps are involved in kernel PCA.…”

Section: Kernel Principal Component Analysismentioning

confidence: 99%

Computer assisted diagnosis of bipolar disorder using invariant features

Jiji¹,

Selvaraj²

2022

Concurrency and Computation

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In the study, a supervised learning framework is focused to identify the bipolar disorder (BD) using structural magnetic resonance imaging is focused. The work is based on the newly developed 3D SIFT and 3D SURF feature vectors with pattern recognition technique. The overall hypothesis is to deduct BD results from dysfunctional cellular metabolism within specific brain systems (i.e., anterior limbic brain network) as reflected in abnormalities in brain activation patterns and in specific neurochemical measures. The proposed method is used to integrate neuroimaging in exploring the biomarkers of BD to reveal the mechanism. In the method, two newly developed feature vectors and kernel PCA are combined or connected to project the feature vectors. Diagnosis process is done by random forest. The results reveal that the method has high potential to identify the BD than earlier works, and an average accuracy of 77.77% is reached. This research reveals that neuroimaging studies will help to differentiate BD from healthy controls.

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“…Predicting defects in a software project is very important to the software development process because the later errors in the software are discovered, the greater the cost of fixing the errors. The purpose of software defect prediction [6][7][8][9][10][11][12] is to help software developers find software defects in the early stages of development to allocate software testing resources reasonably to improve software reliability. The rapid development of machine learning technology allows software testers to build software defect prediction models based on existing data to focus on testing those classes or files that may have defects based on the prediction results [13].…”

Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection

Zhu¹,

Zhu²,

Zhao³

et al. 2022

Journal of Internet Technology

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<p>In the practice of software project development, the developed project is a brand-new project. Defect prediction for this type of software project requires the use of other similar projects (i.e. source projects) to collect relevant data to build a defect prediction model, and make defect prediction for the project under development (i.e. target project). However, the prediction model built with the relevant data of the source project cannot achieve the ideal prediction performance when predicting the target project. The main reason is that there is a large data distribution difference between the source project and the target project. The data distribution difference is mainly in the distribution of features between projects and differences between instances. In response to the above problems, starting from both features and instances, a cross-project defect prediction method is proposed. This method first aligns the feature distribution based on the data of the existing target project and the source project data. Then, it selects the labeled instance that is similar to the unlabeled instance in the target project, and finally builds a defect prediction model based on the selected source project instances. Cross-project defect prediction experiments were carried out on the Relink datasets and the Promise datasets. Compared with the classic instance-based cross-project defect prediction method, significant improvements have been made in F-measure and AUC; compared with the prediction of within project defect prediction, it has achieved comparable performance.</p> <p> </p>

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

Cited by 24 publications

References 87 publications

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Computer assisted diagnosis of bipolar disorder using invariant features

Cross-Project Defect Prediction Method based on Feature Distribution Alignment and Neighborhood Instance Selection

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