Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets

Nekooeimehr, Iman; Lai-Yuen, Susana K.

doi:10.1016/j.eswa.2015.10.031

Cited by 233 publications

(89 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5), the G - mean (Eq. 6), the receiver operating characteristic (ROC) curve, the precision–recall (PR) curve, and the area under ROC curve (AUC) to evaluate our classifier comprehensively under the imbalanced dataset scenario [9, 20–22]. Because the F1 - measure and G - mean [33, 34] simultaneously considers the accuracy for both the positive and negative classes, their values will be very low when the classifier underemphasizes the minority class and overemphasizes the majority class. …”

Section: Methodsmentioning

confidence: 99%

“…Generally, two types of approaches, namely, data leveling [18–20] and algorithm levelling [9, 21, 22] are employed to address the imbalanced datasets problem. Over- or down-sampling methods used at the data level attempt to balance the majority and minority class proportions by data resampling to address the imbalanced problem.…”

Section: Introductionmentioning

confidence: 99%

“…Over- or down-sampling methods used at the data level attempt to balance the majority and minority class proportions by data resampling to address the imbalanced problem. However, this approach can easily lead to redundant or missing information and thus affect the classification performance [20, 21, 23]. By contrast, the cost-sensitive approach using algorithm leveling has a distinct advantage because it makes full use of the original data [9, 21, 22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

et al. 2017

View full text Add to dashboard Cite

BackgroundOcular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial.MethodsIn this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient.ResultsQualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method.ConclusionOur study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.Electronic supplementary materialThe online version of this article (10.1186/s12938-017-0420-1) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The existing oversampling methods used for comparison include SMOTE , borderline‐SMOTE , MWMOTE , A‐SUWO , ADASYN and EFS‐SMOTE . As discussed in Ref.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Nekooeimehr et al . proposed an adaptive semi‐unsupervised weighted oversampling (A‐SUWO) method that identifies hard‐to‐learn instances by considering minority instances from each sub‐cluster that are closer to the borderline. However, the oversampling by linear interpolation is not very suitable when dealing with nonlinear problems because it may introduce an overlap between classes [8].…”

Section: Introductionmentioning

confidence: 99%

Oversampling the minority class in a multi‐linear feature space for imbalanced data classification

Liang

2018

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This paper proposes a novel oversampling method for imbalanced data classification, in which the minority class samples are synthesized in a feature space to avoid the generated minority samples falling into the majority class regions. For this purpose, it introduces a multi-linear feature space (MLFS) based on a quasi-linear kernel, which is composed from a pretrained neural network (NN). By using the quasi-linear kernel, the proposed MLFS oversampling method avoids computing directly the Euclidean distances among the samples when oversampling the minority class and mapping the samples to high-dimensional feature space, which makes it easy to be applied to classification of high-dimensional datasets. On the other hand, by using kernel learning instead of representation learning using the NN, it makes an unsupervised learning, even a transfer learning, to be easily employed for the pretraining of NNs because a kernel is usually less dependent on a specific problem, which makes it possible to avoid considering the imbalance problem at the stage of pretraining the NN. Finally, a method is developed to oversample the synthetic minority samples by computing the quasi-linear kernel matrix instead of computing very high dimensional MLFS feature vectors directly. The proposed MLFS oversampling method is applied to different real-world datasets including image dataset, and simulation results confirm the effectiveness of the proposed method.

show abstract

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Susan

Kumar

2020

Engineering Reports

View full text Add to dashboard Cite

This survey paper focuses on one of the current primary issues challenging data mining researchers experimenting on real-world datasets. The problem is that of imbalanced class distribution that generates a bias toward the majority class due to insufficient training samples from the minority class. The current machine learning and deep learning algorithms are trained on datasets that are insufficiently represented in certain categories. On the other hand, some other classes have surplus samples due to the ready availability of data from these categories.Conventional solutions suggest undersampling of the majority class and/or oversampling of the minority class for balancing the class distribution prior to the learning phase. Though this problem of uneven class distribution is, by and large, ignored by researchers focusing on the learning technology, a need has now arisen for incorporating balance correction and data pruning procedures within the learning process itself. This paper surveys a plethora of conventional and recent techniques that address this issue through intelligent representations of samples from the majority and minority classes, that are given as input to the learning module. The application of nature-inspired evolutionary algorithms to intelligent sampling is examined, and so are hybrid sampling strategies that select and retain the difficult-to-learn samples and discard the easy-to-learn samples. The findings by various researchers are summarized to a logical end, and various possibilities and challenges for future directions in research are outlined. K E Y W O R D Sclass-imbalance problem, hybrid sampling, imbalanced data, oversampling, sampling, undersampling INTRODUCTIONLearning from imbalanced datasets results in a bias toward the majority class whose labeled samples are available in plenty as compared to the insufficiently represented minority class. 1 In data mining, factors that bring down the classifier performance are the intrinsic characteristics of the data and an uneven class distribution. 2 Lack of adequate data in the minority class results in a fuzzy and ever-varying decision boundary, leading to erroneous results. The problem isThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Adaptive semi-unsupervised weighted oversampling (A-SUWO) for imbalanced datasets

Cited by 233 publications

References 21 publications

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Oversampling the minority class in a multi‐linear feature space for imbalanced data classification

The balancing trick: Optimized sampling of imbalanced datasets—A brief survey of the recent State of the Art

Contact Info

Product

Resources

About