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
| Chapter title |
Learning Interpretable Rules for Multi-Label Classification
|
|---|---|
| Chapter number | 4 |
| Book title |
Explainable and Interpretable Models in Computer Vision and Machine Learning
|
| Published in |
arXiv, November 2018
|
| DOI | 10.1007/978-3-319-98131-4_4 |
| Book ISBNs |
978-3-31-998130-7, 978-3-31-998131-4
|
| Authors |
Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp, Mencía, Eneldo Loza, Fürnkranz, Johannes, Hüllermeier, Eyke, Rapp, Michael |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Student > Master | 5 | 16% |
| Professor > Associate Professor | 3 | 10% |
| Lecturer | 2 | 6% |
| Researcher | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 7 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 55% |
| Business, Management and Accounting | 2 | 6% |
| Agricultural and Biological Sciences | 1 | 3% |
| Medicine and Dentistry | 1 | 3% |
| Engineering | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 8 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 05 December 2018.
All research outputs
#16,293,793
of 24,002,307 outputs
Outputs from arXiv
#406,013
of 1,011,770 outputs
Outputs of similar age
#272,301
of 443,787 outputs
Outputs of similar age from arXiv
#12,219
of 26,049 outputs
Altmetric has tracked 24,002,307 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,011,770 research outputs from this source. They receive a mean Attention Score of 4.0. This one has gotten more attention than average, scoring higher than 52% of its peers.
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 443,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26,049 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.