Heterogeneous Uncertainty Sampling for Supervised Learning

Lewis, David D., Catlett, Jason·1994·Machine Learning Proceedings 1994

DOI 10.1016/b978-1-55860-335-6.50026-x

Open this paper in CoChat — free account

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/b978-1-55860-335-6.50026-x

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Untitled work
(2009) · Minds at UW (University of Wisconsin) · Cited by 4,791
Borderline-SMOTE : A New Over-Sampling Method in Imbalanced Data Sets Learning
Han, H., Wang, W.-Y., & Mao, B.-H. (2005) · Lecture notes in computer science · doi:10.1007/11538059_91 · Cited by 4,331
The class imbalance problem: A systematic study1
Japkowicz, N., & Stephen, S. (2002) · Intelligent Data Analysis · doi:10.3233/ida-2002-6504 · Cited by 3,295
A Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208
Self-citation
Learning when training data are costly: the effect of class distribution on tree induction
Weiss, G. M., & Provost, F. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1199 · Cited by 921
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776
Learning Loss for Active Learning
Yoo, D., & Kweon, I. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00018 · Cited by 740
A survey on active learning and human-in-the-loop deep learning for medical image analysis
Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606
Active learning
Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605
On Issues of Instance Selection
Liu, H., & Motoda, H. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014056429969 · Cited by 191
Contextual Diversity for Active Learning
Agarwal, S., Arora, H., Anand, S., & Arora, C. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58517-4_9 · Cited by 176
Multiple Instance Active Learning for Object Detection
Yuan, T., Wan, F., Fu, M., Liu, J., Xu, S., Ji, X., & Ye, Q. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00529 · Cited by 155
Localization-Aware Active Learning for Object Detection
Kao, C.-C., Lee, T.-Y., Sen, P., & Liu, M.-Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20876-9_32 · Cited by 118
Applying active learning to assertion classification of concepts in clinical text
Chen, Y., Mani, S., & Xu, H. (2012) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.11.003 · Cited by 57
On modelling label uncertainty in deep neural networks: Automatic estimation of intra-observer variability in 2d echocardiography quality assessment
Liao, Z., Girgis, H., Abdi, A., Vaseli, H., Hetherington, J., Rohling, R., Gin, K., Tsang, T., & Abolmaesumi, P. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2959209 · Cited by 56
Active and semi-supervised learning for object detection with imperfect data
Rhee, P. K., Erdenee, E., Kyun, S. D., Ahmed, M. U., & Jin, S. (2017) · Cognitive Systems Research · doi:10.1016/j.cogsys.2017.05.006 · Cited by 55
Deep Active Learning for Computer Vision: Past and Future
Takezoe, R., Liu, X., Mao, S., Chen, M. T., Feng, Z., Zhang, S., & Wang, X. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000057 · Cited by 26
Bayesian active probabilistic classification for psychometric field estimation
Song, X. D., Sukesan, K. A., & Barbour, D. L. (2018) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-017-1460-0 · Cited by 22
Active learning for e-rulemaking: public comment categorization
(2008) · Scholarship @ Cornell Law (Cornell University) · doi:10.5555/1367832.1367873 · Cited by 13
Building a large gene expression-cancer knowledge base with limited human annotations
Marchesin, S., Menotti, L., Giachelle, F., Silvello, G., & Alonso, O. (2023) · Database · doi:10.1093/database/baad061 · Cited by 4
10.1162/153244302760185243
(2000) · CrossRef Listing of Deleted DOIs · doi:10.1162/153244302760185243

Authors: Lewis, D. D., & Catlett, J.
Venue: Machine Learning Proceedings 1994
Publisher: Elsevier
Published: 1994
Pages: 148-156
DOI: 10.1016/b978-1-55860-335-6.50026-x
OpenAlex: W1513874326
Cited by: 1,150
References: 0
FWCI: 16.27
Type: book chapter
Language: en
Open access: closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review