A Novel Imbalanced Ensemble Learning in Software Defect Predication

Zheng, Jinfeng; Wang, Xingqi; Wei, Dan; Bin, Chen; Shao, Yanli

doi:10.1109/access.2021.3072682

Cited by 11 publications

(2 citation statements)

References 42 publications

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“…But it failed to focus on the redundant features of high-dimensional datasets. A new imbalanced ensemble learning was introduced in [14] for software defect prediction. But the time consumption of software defect predication was not minimized efficiently.…”

Section: Related Workmentioning

confidence: 99%

Piecewise Congruence Regressed Indexive Extreme Learning classifier for Software Fault Prediction

Sureka

Venkatesh

2023

Preprint

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Early detection of software faults is significant task in the software development process. With the development of defect prediction technology, software with defects negatively impacts on operational costs and finally affects client satisfaction. Therefore, early defect prediction by the project managers is critical and challenging task. Different approaches were developed to predict software faults. However two essential factors are major issues such as timely and accurate detection. Therefore, a novel technique is required for accurate and timely software fault production. In this paper, a Piecewise Congruence Regressed Indexive Extreme Learning Classifier (PRILEC) is introduced for accurate software fault prediction with minimum time. The PRILEC technique consists of two major processes namely feature selection or software metric selection from the input dataset and the classification. In the first process, feature selection or software metric selection process is carried out by using congruence correlative piecewise regression. First, the numbers of features or metrics are collected from the dataset. Then the correlation between the features is measured using congruence coefficient and identifies the more relevant features. With the selected features, the classification is performed by applying statistical indexive levenberg extreme learning classifier for fault prediction with higher accuracy. In the extreme learning classifier, the testing and raining data analysis is performed with the help of Camargo's statistical index. Then the Hardlimit activation function is applied to identify the defective or non-defective software code. Finally, the Levenberg–Marquardt algorithm is applied to minimize the least square problem and obtain the final better classification results at output layer. In this way, software fault prediction is carried out with higher accuracy and minimum time. Experimental assessment of the proposed technique is carried out with respect to fault prediction accuracy, precision, recall, F-measure, prediction, specificity and time complexity with a different number of data. The quantitatively discussed results indicate that the performance of proposed technique increases data accuracy of software fault prediction with a higher minimum time as well as space complexity than the conventional method.

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Section: Related Workmentioning

confidence: 99%

Piecewise Congruence Regressed Indexive Extreme Learning classifier for Software Fault Prediction

Sureka

Venkatesh

2023

Preprint

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“…Nevertheless, not only do the costs of misclassifying various classes depend on specific data, but it is also frequently difficult to measure precisely. Algorithm-level approaches improve learning in the minority class by enhancing or designing new algorithms to deal with imbalanced issues [10]. Data-level approaches directly manipulate datasets by resampling to equalise class number disparities [11].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive and Robust Method for Oriented Oversampling With Spatial Information for Imbalanced Noisy Datasets

Deng,

2023

IEEE Access

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Imbalanced datasets have a large negative impact on the classifiers, biasing the classification results towards the majority class. Since imbalanced data distribution is an inevitable and significant challenge in the real world, many variants of SMOTE have been proposed. However, current oversampling methods still need improvement because they rely on hyperparameter optimization, overgeneralize due to emphasizing specific synthetic regions, randomly synthesize samples or suffer from noise performance degradation. To overcome the above problems, we propose an adaptive and robust method (OOSI) for oriented oversampling with spatial information to deal with imbalanced noisy datasets. OOSI is a rare adaptive and effective oversampling method that can fill the gaps of existing methods through datasetspecific spatial partitioning and information quantization, three-stage noise suppression, and spatiallyinformed generation path improvement. Firstly, a specific and adaptive clustering space is adaptively derived through the data space division of the characteristics of datasets. Then, all minority clusters are assigned a reasonable number of synthetic samples to simultaneously address intra-and inter-class imbalances by integrating the cluster samples' intra-cluster sparsity and the multi-class density information. After differentiating and identifying the noise, oriented weights are assigned based on the multi-class information level to guide the enhancement of the generation path of the synthetic samples and prevent the generation of extra noisy and overlapping samples. Extensive experiments demonstrate that the proposed algorithm outperforms 11 prominent oversampling algorithms on 11 real-world datasets with varying noise levels.INDEX TERMS Imbalanced learning, label noise, oriented oversampling,

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Light Gradient Boosting Machine in Software Defect Prediction: Concurrent Feature Selection and Hyper Parameter Tuning

Kumar¹,

Nayak²,

Behera³

et al. 2022

Intelligent Sustainable Systems

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A Novel Imbalanced Ensemble Learning in Software Defect Predication

Cited by 11 publications

References 42 publications

Piecewise Congruence Regressed Indexive Extreme Learning classifier for Software Fault Prediction

Piecewise Congruence Regressed Indexive Extreme Learning classifier for Software Fault Prediction

An Adaptive and Robust Method for Oriented Oversampling With Spatial Information for Imbalanced Noisy Datasets

Light Gradient Boosting Machine in Software Defect Prediction: Concurrent Feature Selection and Hyper Parameter Tuning

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