Oversampling imbalanced data in the string space

Castellanos, Francisco J.; Valero-Mas, Jose J.; Calvo-Zaragoza, Jorge; Rico-Juan, Juan Ramón

doi:10.1016/j.patrec.2018.01.003

Cited by 46 publications

(21 citation statements)

References 33 publications

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“…Undersampling techniques such as random undersampling [4,5], which is the simplest sampling method, attempt to eliminate the harms of the imbalanced distribution by removing the intrinsic examples in the majority class. Contrary to undersampling, oversampling techniques aim to create new minority class examples [6][7][8][9][10]. For example, the random oversampling technique increases the number of examples of the minority class by randomly duplicating the minority class examples, and the synthetic minority oversampling technique (SMOTE) increases the ones by creating new synthetic examples along the line between the minority examples and their selected nearest neighbors [7,8].…”

Section: Introductionmentioning

confidence: 99%

Improving undersampling-based ensemble with rotation forest for imbalanced problem

Guo¹,

Diao²,

Liu³

2019

Turk J Elec Eng & Comp Sci

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As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size of the dataset to train each classifier is notably small; thus, how to generate individual classifiers with high performance from the limited data is a key to the success of the method. In this paper, rotation forest (an ensemble method) is used to improve the performance of the undersampling-based ensemble on the imbalanced problem because rotation forest has higher performance than other ensemble methods such as bagging, boosting, and random forest, particularly for small-sized data. In addition, rotation forest is more sensitive to the sampling technique than some robust methods including SVM and neural networks; thus, it is easier to create individual classifiers with diversity using rotation forest. Two versions of the improved undersampling-based ensemble methods are implemented: 1) undersampling subsets from the majority class and learning each classifier using the rotation forest on the data obtained by combing each subset with the minority class and 2) similarly to the first method, with the exception of removing the majority class examples that are correctly classified with high confidence after learning each classifier for further consideration. The experimental results show that the proposed methods show significantly better performance on measures of recall, g-mean, f-measure, and AUC than other state-of-the-art methods on 30 datasets with various data distributions and different imbalance ratios.

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Section: Introductionmentioning

confidence: 99%

Improving undersampling-based ensemble with rotation forest for imbalanced problem

Guo¹,

Diao²,

Liu³

2019

Turk J Elec Eng & Comp Sci

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“…And it can produce a better feature representation by unsupervised feature learning. In view of the second strategy, the data-based method [ 21 – 24 ] concentrates on generating new samples for minority class (oversampling) or removing samples from majority class (undersampling). Resampling techniques are often employed as a preprocessing process.…”

Section: Introductionmentioning

confidence: 99%

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN

2018

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BackgroundImbalanced data classification is an inevitable problem in medical intelligent diagnosis. Most of real-world biomedical datasets are usually along with limited samples and high-dimensional feature. This seriously affects the classification performance of the model and causes erroneous guidance for the diagnosis of diseases. Exploring an effective classification method for imbalanced and limited biomedical dataset is a challenging task.MethodsIn this paper, we propose a novel multilayer extreme learning machine (ELM) classification model combined with dynamic generative adversarial net (GAN) to tackle limited and imbalanced biomedical data. Firstly, principal component analysis is utilized to remove irrelevant and redundant features. Meanwhile, more meaningful pathological features are extracted. After that, dynamic GAN is designed to generate the realistic-looking minority class samples, thereby balancing the class distribution and avoiding overfitting effectively. Finally, a self-adaptive multilayer ELM is proposed to classify the balanced dataset. The analytic expression for the numbers of hidden layer and node is determined by quantitatively establishing the relationship between the change of imbalance ratio and the hyper-parameters of the model. Reducing interactive parameters adjustment makes the classification model more robust.ResultsTo evaluate the classification performance of the proposed method, numerical experiments are conducted on four real-world biomedical datasets. The proposed method can generate authentic minority class samples and self-adaptively select the optimal parameters of learning model. By comparing with W-ELM, SMOTE-ELM, and H-ELM methods, the quantitative experimental results demonstrate that our method can achieve better classification performance and higher computational efficiency in terms of ROC, AUC, G-mean, and F-measure metrics.ConclusionsOur study provides an effective solution for imbalanced biomedical data classification under the condition of limited samples and high-dimensional feature. The proposed method could offer a theoretical basis for computer-aided diagnosis. It has the potential to be applied in biomedical clinical practice.

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“…A lot of imbalanced learning algorithms have been developed over the past decade. To design algorithms that can deal with the class-imbalance problem, several approaches are widely adopted, such as the resampling approach (Zhu et al, 2017;Castellanos et al, 2018), the cost-sensitive approach (Cheng et al, 2016) and the ensemble approach (Sun et al, 2015;Lusa et al, 2016;Tang and He, 2017;Yuan et al, 2018). Most of imbalanced learning algorithms are designed to solve binary clas-In imbalanced learning, the class-imbalance extent is an important measurement to describe how imbalanced the data are (Ortigosa-Hernández et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test

Zhu

Wang

et al. 2018

Pattern Recognition Letters

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In this paper, we introduce a new likelihood ratio imbalance degree (LRID) to measure the class-imbalance extent of multi-class data. Imbalance ratio (IR) is usually used to measure class-imbalance extent in imbalanced learning problems. However, IR cannot capture the detailed information in the class distribution of multi-class data, because it only utilises the information of the largest majority class and the smallest minority class. Imbalance degree (ID) has been proposed to solve the problem of IR for multi-class data. However, we note that improper use of distance metric in ID can have harmful effect on the results. In addition, ID assumes that data with more minority classes are more imbalanced than data with less minority classes, which is not always true in practice. Thus ID cannot provide reliable measurement when the assumption is violated. In this paper, we propose a new metric based on the likelihood-ratio test, LRID, to provide a more reliable measurement of class-imbalance extent for multi-class data. Experiments on both simulated and real data show that LRID is competitive with IR and ID, and can reduce the negative correlation with F1 scores by up to 0.55.

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Oversampling imbalanced data in the string space

Cited by 46 publications

References 33 publications

Improving undersampling-based ensemble with rotation forest for imbalanced problem

Improving undersampling-based ensemble with rotation forest for imbalanced problem

Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN

LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test

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