Hybrid Random Under-Sampling Approach in MRI Compressed Sensing

Nguyen, Thang Van; Huy, Trần Quang; Nguyễn, Vân Dung; Thu, Nguyen Thị; Anh, Gian Quoc; Tran, Duc–Tan

doi:10.1007/978-981-15-2780-7_99

Cited by 1 publication

(2 citation statements)

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“…From the current research outputs, we can see that the research of undersampling methods have achieved remarkable results. The undersampling methods are mainly based on RUS to discard some majority class data according to the distribution of data to re‐balance data 32,33 . There is also quite a lot of research on clustering methods which dig out the data distribution and retain the useful samples through clustering 16,18 .…”

Section: Related Workmentioning

confidence: 99%

“…The undersampling methods are mainly based on RUS to discard some majority class data according to the distribution of data to re-balance data. 32,33 There is also quite a lot of research on clustering methods which dig out the data distribution and retain the useful samples through clustering. 16,18 However, the clustering methods need to define the cluster number first.…”

Section: Related Workmentioning

confidence: 99%

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Undersampling of approaching the classification boundary for imbalance problem

Jiang

Yuan

Liao

et al. 2022

Concurrency and Computation

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Using imbalanced data in classification affect the accuracy. If the classification is based on imbalanced data directly, the results will have large deviations. A common approach to dealing with imbalanced data is to re-structure the raw dataset via undersampling method. The undersampling method usually uses random or clustering approaches to trimming the majority class in the dataset, since some data in the majority class makes not contribute to classification model. In this paper a revised undersampling approach is proposed. First, we perform space compression in the vertical direction of the separating hyperplane. Then, a weighted random sampling hybrid ensemble learning method is carried out to make the sampled objects spread more widely near the separating hyperplane. Experiments with 7 under-sampling methods on 21 imbalanced datasets show that our method has achieved good results.

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Section: Related Workmentioning

confidence: 99%