Minority oversampling for imbalanced ordinal regression

Zhu, Tuanfei; Lin, Yaping; Liu, Yonghe; Zhang, Wei; Zhang, Jianming

doi:10.1016/j.knosys.2018.12.021

Cited by 32 publications

(14 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The Cluster-Based Weighted Over-sampling clusters minority classes at first, and then oversamples them based on their distance, and finally sorts the classes [7]. Synthetic Minority oversampling technique to deal exclusively with imbalanced Ordinal Regression (SMOR) is a directionaware oversampling algorithm [8], and it can effectively avoid wrong synthetic samples generation by considering the rank of the classes. SMOR computes the selection weight of being used to generate synthetic samples for each candidate generation direction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: A Experimental Setup 1) Design Of Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active Learning for Imbalanced Ordinal Regression

Chen

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…An exclusive technique for ordinal regression imbalanced problem also uses oversampling approach for data generation in minority class based on the weights assigned. This synthetic minority oversampling for ordinal regression (SMOR) considers www.ijacsa.thesai.org generation direction for each candidate [41]. Another oversampling technique for imbalances problem in ordinal regression is proposed which is adaptive structure based.…”

Section: Related Workmentioning

confidence: 99%

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

Ali¹,

Najib²,

Hussain³

2019

IJACSA

View full text Add to dashboard Cite

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].

show abstract

Minority oversampling for imbalanced ordinal regression

Cited by 32 publications

References 46 publications

Active Learning for Imbalanced Ordinal Regression

Active Learning for Imbalanced Ordinal Regression

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

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

Contact Info

Product

Resources

About