Multiview Transfer Learning for Software Defect Prediction

Chen, Jinyin; Yang, Yitao; Hu, Keke; Xuan, Qi; Liu, Yi; Yang, Chao

doi:10.1109/access.2018.2890733

Cited by 37 publications

(28 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After all, when ML techniques are applied to each dataset, the outcomes are assessed using different assessment measures to show the better performance of an individual technique. For this, nine assessment measures, namely, RMSE [ 22 – 24 ], RRSE [ 25 ], specificity [ 26 – 28 ], precision [ 29 – 31 ], recall [ 27 , 29 , 32 ], F-measure [ 29 , 30 , 33 ], G-measure [ 22 , 34 ], MCC [ 29 , 35 , 36 ], and accuracy [ 3 , 37 , 38 ], are utilized to assess the performance of ML classification algorithm going on liver datasets.…”

Section: Methodsmentioning

confidence: 99%

“…A model may produce satisfactory results when it is assessed using standard assessment measures. However, in this study, two types of assessment measures are used in which some are utilized for evaluating error rate that includes RMSE [ 25 , 39 ] and RRSE [ 25 ], while others are employed for the assessment of accuracy that comprises specificity [ 5 , 40 ], precision [ 32 , 41 ], recall [ 31 , 42 ], F-measure [ 29 , 36 ], G-measure [ 22 , 34 ], MCC [ 29 , 35 , 36 ], and accuracy [ 3 , 37 , 38 ]. Table 3 shows the equation for calculating each assessment measure with equations, where | y i − y | is the absolute error, n is the number of errors, T j is the goal value for record ji , P ij is the prediction rate by the particular model I for data j (out of n records), TP presents the true-positive classification, FN shows the false-negative classification, TN grants the true-negative classification, and FP is the rate of false-positive classifications.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Naseem

Khan

Shah

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Naseem

Khan

Shah

et al. 2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Eq. (6) indicates that the pooling layer power consumption is also largely determined by the DNN architecture.…”

Section: A Dnn Power Modelsmentioning

confidence: 99%

Open DNN Box by Power Side-Channel Attack

Xiang

Chen

et al. 2020

IEEE Trans. Circuits Syst. II

Self Cite

View full text Add to dashboard Cite

Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable, i.e., they are black-box. Therefore, in this work, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) we are the first to use side-channel information to reveal internal network architecture in embedded devices;(2) we are the first to construct models for internal parameter estimation; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI applications, and further propose corresponding defensive strategies in the future.Index Terms-Deep learning, machine learning, model identification, side-channel attack, adversarial attacks.

show abstract

“…Wen et al adopted feature selection, which combined with transfer component analysis (TCA+) for spatial transformation, and obtained accurate prediction results [10]. Chen et al proposed a heterogeneous data orienting multiview transfer learning for software defect prediction, which can achieve different dimensions and granularities metrics to automatically learn labels through neural network models [11]. Chen et al proposed a collective training mechanism for defect prediction (CTDP), which made the distributions of source and target projects similar to each other by transfer learning [12].…”

Section: Introductionmentioning

confidence: 99%

Few-Shot Learning Based Balanced Distribution Adaptation for Heterogeneous Defect Prediction

Wang

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Heterogeneous defect prediction (HDP) aims to predict the defect tendency of modules in one project using heterogeneous data collected from other projects. It sufficiently incorporates the two characteristics of the defect prediction data: (1) datasets could have different metrics and distribution, and (2) data could be highly imbalanced. In this paper, we propose a few-shot learning based balanced distribution adaptation (FSLBDA) approach for heterogeneous defect prediction, which takes into consideration the two characteristics of the defect prediction data. Class imbalance of the defect datasets can be solved with undersampling, but the scale of the training datasets will be smaller. Specifically, we first remove redundant metrics of datasets with extreme gradient boosting. Then, we reduce the data difference between the source domain and the target domain with the balanced distribution adaptation. It considers the marginal distribution and the probability of conditional distribution differences and adaptively assigns different weights to them. Finally, we use adaptive boosting to relieve the influence caused by the size of the training dataset is smaller, which can improve the accuracy of the defect prediction model. We conduct experiments on 17 projects from 4 datasets using 3 indicators (i.e., AUC, G-mean, F-measure). Compared to three classic approaches, the experimental results show that FSLBDA can effectively improve the prediction performance.

show abstract

Multiview Transfer Learning for Software Defect Prediction

Cited by 37 publications

References 58 publications

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

Open DNN Box by Power Side-Channel Attack

Few-Shot Learning Based Balanced Distribution Adaptation for Heterogeneous Defect Prediction

Contact Info

Product

Resources

About