Evidence map›Paper›PMID 39660055›Full record

ArticleiScience2024

Unsupervised attribute reduction based on variable precision weighted neighborhood dependency.

Yi Li, Benwen Zhang, Hongming Mo, Jiancheng Hu, Yuncheng Liu, Xingqiang Tan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. 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.

  1. 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.

Yi LiInstitute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.
Benwen ZhangInstitute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.
Hongming MoInstitute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.
Jiancheng HuCollege of Applied Mathematics, Chengdu University of Information Technology, Chengdu 610225, China.
Yuncheng LiuSchool of Mathematics, Southwest Minzu University, Chengdu 610041, China.
Xingqiang TanInstitute of Computer Application Research, Sichuan Minzu College, Kangding 626001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neighborhood rough set (NRS) have been successfully applied to attribute reduction (AR). However, most current methods of AR based on NRS are supervised or semi-supervised. This limits their ability to process data without decision information. When granulating data samples, NRS considers only the number of samples within the neighborhood radius. It does not consider distribution information between samples, which can result in the loss of original data information. To address the aforementioned issue, we propose an unsupervised attribute reduction (UAR) strategy based on variable precision weighted neighborhood dependency (VPWND) (UAR_VPWND). We compare algorithm UAR_VPWND to existing classical UAR algorithms using public datasets. The experimental results show that algorithm UAR_VPWND can select fewer attributes to maintain or improve the performance of clustering learning algorithms.

Indexed as

Artificial intelligenceComputational mathematicsComputer science

Identifiers

PMID39660055
PMCPMC11629270

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.