Synthetic minority oversampling technique for multiclass imbalance problems

Zhu, Tuanfei; Lin, Yaping; Liu, Yonghe

doi:10.1016/j.patcog.2017.07.024

Cited by 212 publications

(71 citation statements)

References 17 publications

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“…To show the availability of our sampling strategy, we compare the performance of our algorithms (including AL-OR and IAL-IOR) with the random sampling (randomly select query samples). To show the generalization of our algorithm, we compare the performance of our algorithms with the stateof-the-art imbalance methods (including under-sampling (US) and an over-sampling methods (SMOTE [4])) and recent proposed imbalanced methods (including SMOR [8] and SMOM [41]). The performance of all algorithms will be [8] and SMOM [41] is available at the website https://github.com/zhutuanfei/SMOR, and the code of our algorithms is available at the website https://github.com/gjmrookie/active-learning-forimbalanced-OR.…”

Section: A Experimental Setup 1) Design Of Experimentsmentioning

confidence: 99%

Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

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Ordinal regression (OR), also called ordinal classification, is a special multi-classification designed for problems with ordered classes. Imbalanced data hinders the performance of classification algorithms, especially for OR algorithms, as imbalanced class distributions often arise in OR problems. In this paper, we address an active learning based solution for imbalanced OR problem. We propose an active learning algorithm for OR (AL-OR) to select the most informative samples from unlabeled samples, mark them and add them to the training set. Based on AL-OR, we put forward an improved active learning for imbalanced OR (IAL-IOR), which further adjust the sampling strategy of AL-OR dynamically to make the training data as valuable and balanced as possible. Recall rate for multi-classification and new mean absolute error are designed to measure the performance of the algorithms. To the best of our knowledge, our algorithm is the first algorithm for imbalanced OR in algorithm level. The experimental results show that the proposed algorithms have faster convergence and much better generalization ability than the classical methods and the state-of-the-art methods under the evaluation measurements for imbalance problems. In addition, we also proved the effectiveness of our algorithms by statistical analysis.

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Section: A Experimental Setup 1) Design Of Experimentsmentioning

confidence: 99%

Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

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“…Other popular over‐sampling approaches include Borderline‐SMOTE (BLSMOTE) by Han et al, 17 Safe‐Level‐SMOTE (SLSMOTE) by Bunkhumpornpat et al, 18 RACOG (rapidly converging the Gibbs algorithm) by Das et al, 16 MDO (Mahalanobis distance‐based over‐sampling algorithm) by Abdi and Hashemi, 19 A‐SUWO (adaptive semi‐unsupervised weighted over‐sampling algorithm) by Nekooeimehr and Lai‐Yuen 20 and SMOM ( k ‐nearest neighbours‐based synthetic minority over‐sampling algorithm) by Zhu et al, 21 and so forth. However, due to the limited space of this paper, it is impossible to cover all the data sampling approaches in the literature, interested readers may refer to References [6,22,23] for more details.…”

Section: Related Workmentioning

confidence: 99%

A self‐adaptive synthetic over‐sampling technique for imbalanced classification

Angelov

Soares

2020

Int J Intell Syst

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Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest-for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the socalled fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In Xiaowei Gu and Plamen P. Angelov contributed equally to this study.

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“…Douzas et al [47] proposed k-means-SMOTE by combining k-means clustering and SMOTE, which avoids the generation of noise and effectively overcame imbalances between and within classes. Tuanfei Zhu et al successively proposed synthetic minority oversampling for multiclass imbalance (SMOM) [48] and synthetic minority oversampling for imbalanced ordinal regression (SMOR) [49]. SMOM is a k-NN based synthetic minority oversampling algorithm which assigns a selection weight to each neighbor direction.…”

Section: Approaches For Imbalanced Data Classificationmentioning

confidence: 99%

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

Liu

Yang

et al. 2020

IEEE Access

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The detection and removal of malicious social bots in social networks has become an area of interest in industry and academia. The widely used bot detection method based on machine learning leads to an imbalance in the number of samples in different categories. Classifier bias leads to a low detection rate of minority samples. Therefore, we propose an improved conditional generative adversarial network (improved CGAN) to extend imbalanced data sets before applying training classifiers to improve the detection accuracy of social bots. To generate an auxiliary condition, we propose a modified clustering algorithm, namely, the Gaussian kernel density peak clustering algorithm (GKDPCA), which avoids the generation of data-augmentation noise and eliminates imbalances between and within social bot class distributions. Furthermore, we improve the CGAN convergence judgment condition by introducing the Wasserstein distance with a gradient penalty, which addresses the model collapse and gradient disappearance in the traditional CGAN. Three common oversampling algorithms are compared in experiments. The effects of the imbalance degree and the expansion ratio of the original data on oversampling are studied, and the improved CGAN performs better than the others. Experimental results comparing with three common oversampling algorithms show that the improved CGAN achieves the higher evaluation scores in terms of F1-score, G-mean and AUC. INDEX TERMS Social bot detection, conditional generative adversarial networks, data augmentation, supervised classification, imbalanced data.

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Synthetic minority oversampling technique for multiclass imbalance problems

Cited by 212 publications

References 17 publications

Active Learning for Imbalanced Ordinal Regression

Active Learning for Imbalanced Ordinal Regression

A self‐adaptive synthetic over‐sampling technique for imbalanced classification

Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter

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