Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning

Douzas, Georgios; Bação, Fernando

doi:10.1016/j.eswa.2017.03.073

Cited by 153 publications

(54 citation statements)

References 29 publications

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“…Their method addressed the problem of dimensionality that affected earlier methods. Douzas and Bacao [33] introduced a self-organizing map-based method using artificial data points in high-dimensional space. These oversampling methods helped to achieve more samples for the minority class for imbalanced datasets.…”

Section: Related Workmentioning

confidence: 99%

GAN-based synthetic brain PET image generation

2020

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In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer's disease-normal control (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD).

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Section: Related Workmentioning

confidence: 99%

GAN-based synthetic brain PET image generation

2020

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“…Although recent studies demonstrate the usefulness of SMOTE for remote sensing applications, it still has some drawbacks. The SMOTE algorithm has the disadvantage of generating noisy data [27]. In order to mitigate this problem, many variations of SMOTE have been developed.…”

Section: Informed Resamplingmentioning

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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“…The EA benefits in lowering the overall computational cost and in optimization of parameters for data generation method. Self-Organizing Map-based Oversampling (SOMO) [29] is another technique that uses self-organizing map to convert input data into two dimensional space. It generates synthetic instances into effective areas.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome this, various methods have been proposed in the related literature. The effective approaches are clustering-based approaches in which the input space is partitioned into the clusters then the oversampling technique is applied [29]. In some clustering-based approaches, majority and minority classes are clustered separately; possibly when minority class instances shows small disjoints, so it needs to make several minority sub-clusters [30].…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Semi-Unsupervised Weighted Oversampling using Sparsity Factor for Imbalanced Datasets

Ali¹,

Najib²,

Hussain³

2019

IJACSA

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With the incredible surge in data volumes, problems associated with data analysis have been increasingly complicated. In data mining algorithms, imbalanced data is a profound problem in machine learning paradigm. It appears due to desperate nature of data in which, one class with a large number of instances presents the majority class, while the other class with only a few instances is known as minority class. The classifier model biases towards the majority class and neglects the minority class which may happen to be the most essential class; resulting into costly misclassification error of minority class in real-world scenarios. Imbalanced data problem is significantly overcome by using re-sampling techniques, in which oversampling techniques are proven to be more effective than undersampling. This study proposes an Improved Adaptive Semi Unsupervised Weighted Oversampling (IA-SUWO) technique with sparsity factor, which efficiently solves between-the-class and within-the-class imbalances problem. Along with avoiding over-generalization, overfitting problems and removing noise from the data, this technique enhances the number of synthetic instances in the minority sub-clusters appropriately. A comprehensive experimental setup is used to evaluate the performance of the proposed approach. The comparative analysis reveals that the IA-SUWO performs better than the existing baseline oversampling techniques.These methods manipulate the data externally by balancing the distribution of samples among the classes [17] [15].

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Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning

Cited by 153 publications

References 29 publications

GAN-based synthetic brain PET image generation

GAN-based synthetic brain PET image generation

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

Improved Adaptive Semi-Unsupervised Weighted Oversampling using Sparsity Factor for Imbalanced Datasets

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