Evidence map›Paper›PMID 27747600›Full record

ArticleBrain informatics2016

The diversity rank-score function for combining human visual perception systems.

Christina Schweikert, Darius Mulia, Kilby Sanchez, D Frank Hsu

Abstract read
In one paragraph

Article in Brain informatics, 2016. 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
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.

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

4 authors.

Christina SchweikertDivision of Computer Science, Mathematics and Science, St. John's University, Queens, NY, USA. schweikc@stjohns.edu.
Darius MuliaLaboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
Kilby SanchezLaboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.
D Frank HsuLaboratory of Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There are many situations in which a joint decision, based on the observations or decisions of multiple individuals, is desired. The challenge is determining when a combined decision is better than each of the individual systems, along with choosing the best way to perform the combination. It has been shown that the diversity between systems plays a role in the performance of their fusion. This study involved several pairs of people, each viewing an event and reporting an observation, along with their confidence level. Each observer is treated as a visual perception system, and hence an associated scoring system is created based on the observer's confidence. A diversity rank-score function on a set of observation pairs is calculated using the notion of cognitive diversity between two scoring systems in the combinatorial fusion analysis framework. The resulting diversity rank-score function graph provides a powerful visualization tool for the diversity variation among a set of system pairs, helping to identify which system pairs are most likely to show improved performance with combination.

Indexed as

Cognitive diversityCombinatorial fusion analysisDiversity rank-score functionMultiple scoring systemsRank-score characteristic (RSC) function

Identifiers

PMID27747600
PMCPMC4883166

What OpenQuestion holds

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Registered trials

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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.