SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution

Li, Junnan; Zhu, Qingsheng; Wu, Quanwang; Zhang, Zhiyong; Gong, Yanlu; He, Ziqing; Zhu, Fan

doi:10.1016/j.knosys.2021.107056

Cited by 53 publications

(27 citation statements)

References 35 publications

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“…Filtering-based oversampling techniques design noise filters, intending to detect and filter out suspicious noise. SMOTE-ENN [28], SMOTE-WENN [29], SMOTE-IPF [18], FRIPS-SMOTE [19] and SMOTE-NaN-DE [10] are competitive instances with the filtering-based idea. The edited nearest neighbor is employed in SMOTE-ENN and SMOTE-WENN to find mislabeled samples regarded as suspicious noise, in which SMOTE is executed to create synthetic samples.…”

Section: Related Workmentioning

confidence: 99%

“…An improved differential evolution is proposed. Compared with related work [10,30,31], the proposed improved differential evolution is parameter-free and converges faster.…”

Section: Introductionmentioning

confidence: 96%

“…The data-level approach [8] is the most dominant because of the wrapping advantage, i.e., it is independent of classifiers. Concretely, the data-level approach includes oversampling techniques [9,10], undersampling techniques [11] and hybrid techniques [12].…”

Section: Introductionmentioning

confidence: 99%

“…Employed noise detection approaches can find and remove suspicious noise in filtering-based techniques. SMOTE-ENN [28], SMOTE-WENN [29], SMOTE-IPF [18], FRIPS-SMOTE [19] and SMOTE-NaN-DE [10] are with the idea of filtering-based techniques. Despite their effectiveness, they still have the following shortcomings: (b) Most methods fail to handle suspicious noise effectively.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Zhang¹,

2022

IEEE Access

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SMOTE is a classical oversampling method and aims to improve imbalanced classification by creating synthetic minority class samples. Overgeneralization is a great challenge in SMOTE and its improvements. Multiple variations of SMOTE are proposed against imbalances between classes and overgeneralization. However, they still have the following issues: a) most methods depend on too many parameters; b) most methods fail to detect suspicious noise effectively and modify them; c) interpolation of almost all methods is susceptible to abnormal samples. To overcome the above issues, a new synthetic minority oversampling technique based on adaptive local mean vectors and improved differential evolution (SMOTE-LMVDE) is proposed. First, a new noise detection technique based on the defined adaptive local mean vectors (NDALMV) is proposed to find suspicious noise. Second, an improved differential evolution is proposed to modify and improve detected suspicious noise. Finally, a new interpolation based on the defined adaptive local mean vectors is proposed to create synthetic minority class samples. Experiments prove that the proposed method superior to 7 popular oversampling approaches on extensive data sets in the training nearest neighbor classifier and the decision tree classifier.

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Section: Related Workmentioning

confidence: 99%

“…An improved differential evolution is proposed. Compared with related work [10,30,31], the proposed improved differential evolution is parameter-free and converges faster.…”

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Zhang¹,

2022

IEEE Access

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“…Recently, the interest in using EAs to address machine learning problems is growing fastly [19]- [29]. For imbalanced learning, EAs have been used for data sampling [30], [31] and cost-sensitive learning [32]. Although recent studies address the problem of determining the optimal misclassification costs [32], [33], they have paid little attention to considering the hyper-parameters of the learning algorithm, along with exploiting the hierarchical nature of parameter and hyper-parameter learning to guide search.…”

Section: Introductionmentioning

confidence: 99%

Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization

Rosales-Pérez,

García,

Herrera

2022

Preprint

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Support vector machines are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This paper introduces EBCS-SVM: Evolutionary Bilevel Costsensitive Support Vector Machines. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyper-parameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower-level determines the support vectors and the upper-level the hyper-parameters. This optimization problem is solved using an evolutionary algorithm at the upper-level and Sequential Minimal Optimization at the lower-level. These two methods work in a nested fashion, i.e., the optimal support vectors help guide the search of the hyper-parameters, and the lower-level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets.

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Noise simulation in classification with the noisemodel R package: Applications analyzing the impact of errors with chemical data

Sáez

2023

Journal of Chemometrics

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Classification datasets created from chemical processes can be affected by errors, which impair the accuracy of the models built. This fact highlights the importance of analyzing the robustness of classifiers against different types and levels of noise to know their behavior against potential errors. In this context, noise models have been proposed to study noise‐related phenomenology in a controlled environment, allowing errors to be introduced into the data in a supervised manner. This paper introduces the noisemodel R package, which contains the first extensive implementation of noise models for classification datasets, proposing it as support tool to analyze the impact of errors related to chemical data. It provides 72 noise models found in the specialized literature that allow errors to be introduced in different ways in classes and attributes. Each of them is properly documented and referenced, unifying their results through a specific S3 class, which benefits from customized print, summary and plot methods. The usage of the package is illustrated through four application examples considering real‐world chemical datasets, where errors are prone to occur. The software presented will help to deepen the understanding of the problem of noisy chemical data, as well as to develop new robust algorithms and noise preprocessing methods properly adapted to different types of errors in this scenario.

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SMOTE-NaN-DE: Addressing the noisy and borderline examples problem in imbalanced classification by natural neighbors and differential evolution

Cited by 53 publications

References 35 publications

Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization

Noise simulation in classification with the noisemodel R package: Applications analyzing the impact of errors with chemical data

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