FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification

Maldonado, Sebastián; Vairetti, Carla; Fernández, Alberto; Herrera, Francisco

doi:10.1016/j.patcog.2021.108511

Cited by 69 publications

(13 citation statements)

References 40 publications

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“…The Safe-Level-SMOTE method isolates noise or outlier data points before applying the SMOTE procedure. Another recent method, FW-SMOTE, is represented in [ 15 ] that utilized Minkowski distance to specify each positive instance’s neighbor set.…”

Section: Literature Reviewmentioning

confidence: 99%

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Fatlawi

Kiss

2023

Sensors

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Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.

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Section: Literature Reviewmentioning

confidence: 99%

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Fatlawi

Kiss

2023

Sensors

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“…However, the uneven distribution of data was poor. A feature-weighted oversampling approach for imbalanced classification (FW-SMOTE) is an improved SMOTE algorithm 24 in which all the features are weighted, and some of them are selected for the distance calculation to solve the problem of using the Euclidean distance, which may not be suitable for a high-dimensional environment. However, when there are few minority samples and coverage is small, the problem is that the generated samples are too concentrated.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation method based on the Gaussian kernel density for glioma diagnosis with Raman spectroscopy

Wang

Zhou³

2023

Anal. Methods

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“…In this context, SHAP values can be used to explain why a model made a certain prediction for a particular instance by attributing a numerical value to each input feature indicating its contribution to the prediction. 20 By examining these feature importances, we can gain insights into how the model is making its predictions and identify potential areas for improvement or further exploration. SMOTE stands for Synthetic Minority Over-sampling Technique and works by creating synthetic examples of the minority class by interpolating between existing minority class examples.…”

Section: Introductionmentioning

confidence: 99%

“…This technique helps to balance the dataset and improve the performance of ML models trained on imbalanced datasets. SMOTE has been widely used in various applications such as fraud detection, medical diagnosis, and credit risk, 20,[22][23][24] BC rates are found to be on the increase due to its higher incidence, improved diagnosis as well as treatment. BC is typically identified either through screening or the appearance of a symptom such as pain or a palpable mass, which leads to a diagnostic examination.…”

Section: Introductionmentioning

confidence: 99%

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

Ejiyi,

Qin,

Monday

et al. 2023

BioFactors

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Recent research indicates that early detection of breast cancer (BC) is critical in achieving favorable treatment outcomes and reducing the mortality rate associated with it. With the difficulty in obtaining a balanced dataset that is primarily sourced for the diagnosis of the disease, many researchers have relied on data augmentation techniques, thereby having varying datasets with varying quality and results. The dataset we focused on in this study is crafted from SHapley Additive exPlanations (SHAP)‐augmentation and random augmentation (RA) approaches to dealing with imbalanced data. This was carried out on the Wisconsin BC dataset and the effectiveness of this approach to the diagnosis of BC was checked using six machine‐learning algorithms. RA synthetically generated some parts of the dataset while SHAP helped in assessing the quality of the attributes, which were selected and used for the training of the models. The result from our analysis shows that the performance of the models used generally increased to more than 3% for most of the models using the dataset obtained by the integration of SHAP and RA. Additionally, after diagnosis, it is important to focus on providing quality care to ensure the best possible outcomes for patients. The need for proper management of the disease state is crucial so as to reduce the recurrence of the disease and other associated complications. Thus the interpretability provided by SHAP enlightens the management strategies in this study focusing on the quality of care given to the patient and how timely the care is.

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FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification

Cited by 69 publications

References 40 publications

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Data augmentation method based on the Gaussian kernel density for glioma diagnosis with Raman spectroscopy

Breast cancer diagnosis and management guided by data augmentation, utilizing an integrated framework of SHAP and random augmentation

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