Hierarchical Active Learning With Qualitative Feedback on Regions.

Zhipeng Luo, Yazhou He, Yanbing Xue, Hongjun Wang, Milos Hauskrecht, Tianrui Li

Abstract read

In one paragraph

Article in IEEE transactions on human-machine systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Hierarchical Active Learning with Label Proportions on Data Regions.IEEE transactions on knowledge and data engineering · 2024
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Zhipeng LuoSchool of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.ORCID 0000-0002-4053-5443

Yazhou HeDepartment of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.

Yanbing XueWalmart eCommerce, Sunnyvale, CA 94040 USA.

Hongjun WangSchool of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.ORCID 0000-0001-7280-2852

Milos HauskrechtDepartment of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260 USA.ORCID 0000-0002-7818-0633

Tianrui LiManufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory, Sichuan 611756, China.ORCID 0000-0001-7780-104X

Funding

Real-time detection of deviations in clinical care in ICU data streamsR01GM088224 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES, COOPER, GREGORY F. · 2009 to 2017

$3.7M

NIGMS NIH HHS R01 GM088224

6 · The paper itself

Abstract

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to data

Indexed as

Active learning (AL)learning from alternative human feedbacksemisupervised learning

Identifiers

PMID37396345

DOI10.1109/thms.2023.3252815

PMCPMC10310296

What OpenQuestion holds

Textmetadata

LicenceTDM

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.