Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

Douzas, Georgios; Bação, Fernando

doi:10.1016/j.ins.2019.06.007

Cited by 181 publications

(94 citation statements)

References 23 publications

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“…The experimental procedure included a comparison of five oversamplers using five classifiers and three evaluation metrics. Specifically, the oversampling algorithms were Geometric-SMOTE (G-SMOTE) [15], the synthetic minority oversampling technique (SMOTE) [16], Borderline-SMOTE (B-SMOTE) [17], the adaptive synthetic sampling technique (ADASYN) [18] and random oversampling (ROS), while no oversampling was included as a baseline method. Results show that G-SMOTE outperforms every other oversampling technique, for the selected evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

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The automatic production of land use/land cover maps continues to be a challenging problem, with important impacts on the ability to promote sustainability and good resource management. The ability to build robust automatic classifiers and produce accurate maps can have a significant impact on the way we manage and optimize natural resources. The difficulty in achieving these results comes from many different factors, such as data quality and uncertainty. In this paper, we address the imbalanced learning problem, a common and difficult conundrum in remote sensing that affects the quality of classification results, by proposing Geometric-SMOTE, a novel oversampling method, as a tool for addressing the imbalanced learning problem in remote sensing. Geometric-SMOTE is a sophisticated oversampling algorithm which increases the quality of the instances generated in previous methods, such as the synthetic minority oversampling technique. The performance of Geometric- SMOTE, in the LUCAS (Land Use/Cover Area Frame Survey) dataset, is compared to other oversamplers using a variety of classifiers. The results show that Geometric-SMOTE significantly outperforms all the other oversamplers and improves the robustness of the classifiers. These results indicate that, when using imbalanced datasets, remote sensing researchers should consider the use of these new generation oversamplers to increase the quality of the classification results.

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Section: Introductionmentioning

confidence: 99%

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

et al. 2019

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“…[30] Secondly, due to the cohort data being highly imbalanced, with the majority of cases being mild/ordinary, we explored the use of four oversampling methods to address the imbalance distribution issue. [31][32][33][34] Third, we interpreted the importance of features using the SHAP (SHapley Additive exPlanations) framework and identified the features with the highest predictive power. [35] The evaluated predictive models yielded high accuracy and identified predictive imaging and clinical features consistent with prior findings.…”

Section: Background and Significancementioning

confidence: 99%

Severity Assessment of COVID-19 based on Clinical and Imaging Data

Quiroz

Feng

Cheng

et al. 2020

Preprint

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Objectives This study aims to develop a machine learning approach for automated severity assessment of COVID-19 patients based on clinical and imaging data. Materials and Methods Clinical data, including demographics, signs, symptoms, comorbidities and blood test results and chest CT scans of 346 patients from two hospitals in the Hubei province, China, were used to develop machine learning models for automated severity assessment of diagnosed COVID-19 cases. We compared the predictive power of clinical and imaging data by testing multiple machine learning models, and further explored the use of four oversampling methods to address the imbalance distribution issue. Features with the highest predictive power were identified using the SHAP framework. Results Targeting differentiation between mild and severe cases, logistic regression models achieved the best performance on clinical features (AUC:0.848, sensitivity:0.455, specificity:0.906), imaging features (AUC:0.926, sensitivity:0.818, specificity:0.901) and the combined features (AUC:0.950, sensitivity:0.764, specificity:0.919). The SMOTE oversampling method further improved the performance of the combined features to AUC of 0.960 (sensitivity:0.845, specificity:0.929). Discussion Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with findings from previous studies. Oversampling yielded mixed results, although it achieved the best performance in our study. Conclusions This study indicates that clinical and imaging features can be used for automated severity assessment of COVID-19 patients and have the potential to assist with triaging COVID-19 patients and prioritizing care for patients at higher risk of severe cases. [Manuscript last updated on 31 July, 2020]

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“…Similarly, Douzas et al [12] proposed an oversampling method based on k-means clustering and SMOTE. Douzas and Bacao [13] proposed a geometric SMOTE which generates synthetic samples in a hypersphere around each selected minority instance. Mathew et al [14] proposed a weighted kernel-based SMOTE (WK-SMOTE) approach which generates synthetic positive class samples in feature space, WK-SMOTE can overcome the limitation of linear interpolation of SMOTE.…”

Section: Related Workmentioning

confidence: 99%

“…Given a sample x which belongs to cluster A, the Silhouette coefficient of x is defined by Eq. (13).…”

Section: B Performance Evaluation Measuresmentioning

confidence: 99%

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Zhai

Zhang

2020

IEEE Access

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Binary imbalance problem refers to such a classification scenario where one class contains a large number of samples while another class contains only a few samples. When traditional classifiers face with imbalanced datasets, they usually bias towards majority class resulting in poor classification performance. Oversampling is an effective method to address this problem, yet how to conduct diversity oversampling is a challenge. In this paper, we proposed a diversity oversampling method based on a modified D2GAN model, and on the basis of diversity oversampling, we also proposed a binary imbalanced data classification approach based on classifier fusion by fuzzy integral. Extensive experiments are conducted on 8 data sets to compare the proposed methods with 7 state-of-the-art methods on 5 aspects: MMD-score, Silhouette-score, F-measure, G-means, and AUC-area. The 7 methods include 4 SMOTE related approaches and 3 GAN related approaches. The experimental results demonstrate that the proposed methods are more effective and efficient than the compared approaches. INDEX TERMS Binary class imbalance, diversity oversampling, generative adversarial network, classifier fusion, fuzzy integral

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Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE

Cited by 181 publications

References 23 publications

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Imbalanced Learning in Land Cover Classification: Improving Minority Classes’ Prediction Accuracy Using the Geometric SMOTE Algorithm

Severity Assessment of COVID-19 based on Clinical and Imaging Data

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

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