REVIEW 2 cited by
Survey of resampling techniques for improving classification performance in unbalanced datasets
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
A number of classification problems need to deal with data imbalance between classes. Often it is desired to have a high recall on the minority class while maintaining a high precision on the majority class. In this paper, we review a number of resampling techniques proposed in literature to handle unbalanced datasets and study their effect on classification performance.
Forward citations
Cited by 2 Pith papers
-
On the Burstiness of Faces in Set
Bursty faces in a set degrade set-based face recognition, and rebalancing sampling and aggregation to down-weight these faces improves verification accuracy.
-
Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints
Adding an entropy-based boundary weighting term and a density normalization term to Class-Balanced loss improves rare MEP component segmentation on Industrial3D (55.74% mIoU, reducer 0 to 21.12% IoU) versus CB+Focal b...
Discussion (0). Continue with ORCID to comment.