A Decision Probability Transformation Method Based on the Neural Network.

Junwei Li, Aoxiang Zhao, Huanyu Liu

Open access · goldAbstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact, top 83% of its field

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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Junwei LiSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.

Aoxiang ZhaoSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.

Huanyu LiuSchool of Artificial Intelligence, Henan University, Zhengzhou 450046, China.

Henan University · CN

Funding

National Natural Science Foundation of China No.61976080the Key Research Projects of the University in Henan Province, China No.20B510001the Programs for Science and Technology Development of Henan Province, China No.222102210004

6 · The paper itself

Abstract

When the Dempster-Shafer evidence theory is applied to the field of information fusion, how to reasonably transform the basic probability assignment (BPA) into probability to improve decision-making efficiency has been a key challenge. To address this challenge, this paper proposes an efficient probability transformation method based on neural network to achieve the transformation from the BPA to the probabilistic decision. First, a neural network is constructed based on the BPA of propositions in the mass function. Next, the average information content and the interval information content are used to quantify the information contained in each proposition subset and combined to construct the weighting function with parameter

Indexed as

average information contentDempster–Shafer evidence theoryinterval information contentneural networkprobabilistic information content

Identifiers

PMID36421493

DOI10.3390/e24111638

PMCPMC9689871

OpenAlexW4309009617

What OpenQuestion holds

Textmetadata

LicenceCC BY

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.