You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The optimal crowd learning machine
|
|---|---|
| Published in |
BioData Mining, May 2017
|
| DOI | 10.1186/s13040-017-0135-7 |
| Pubmed ID | |
| Authors |
Bilguunzaya Battogtokh, Majid Mojirsheibani, James Malley |
| Abstract |
Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness. For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such. The only assumption needed for proving optimality is that the outcome variable is bounded. The scheme is illustrated using real-world data from the UCI machine learning site, and possible extensions are proposed. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Netherlands | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 38% |
| Student > Ph. D. Student | 3 | 19% |
| Student > Master | 2 | 13% |
| Student > Bachelor | 1 | 6% |
| Librarian | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 3 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 25% |
| Medicine and Dentistry | 2 | 13% |
| Nursing and Health Professions | 1 | 6% |
| Agricultural and Biological Sciences | 1 | 6% |
| Business, Management and Accounting | 1 | 6% |
| Other | 4 | 25% |
| Unknown | 3 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 May 2017.
All research outputs
#13,478,235
of 22,973,051 outputs
Outputs from BioData Mining
#186
of 308 outputs
Outputs of similar age
#158,277
of 312,881 outputs
Outputs of similar age from BioData Mining
#4
of 9 outputs
Altmetric has tracked 22,973,051 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 308 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,881 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.