| Title |
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
|
|---|---|
| Published in |
Machine Learning, August 2000
|
| DOI | 10.1023/a:1007607513941 |
| Authors |
Thomas G. Dietterich |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 19 | 2% |
| United Kingdom | 7 | <1% |
| Germany | 5 | <1% |
| India | 5 | <1% |
| Australia | 4 | <1% |
| Spain | 3 | <1% |
| Belgium | 3 | <1% |
| Russia | 3 | <1% |
| China | 3 | <1% |
| Other | 26 | 2% |
| Unknown | 1043 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 246 | 22% |
| Student > Master | 185 | 17% |
| Researcher | 148 | 13% |
| Student > Bachelor | 71 | 6% |
| Student > Doctoral Student | 58 | 5% |
| Other | 181 | 16% |
| Unknown | 232 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 348 | 31% |
| Engineering | 170 | 15% |
| Agricultural and Biological Sciences | 53 | 5% |
| Environmental Science | 47 | 4% |
| Mathematics | 41 | 4% |
| Other | 180 | 16% |
| Unknown | 282 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 08 September 2022.
All research outputs
#2,082,035
of 25,374,917 outputs
Outputs from Machine Learning
#47
of 1,225 outputs
Outputs of similar age
#1,517
of 38,134 outputs
Outputs of similar age from Machine Learning
#1
of 3 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,225 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 96% 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 38,134 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them