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Mendeley readers
Attention Score in Context
| Title |
An AUC-based permutation variable importance measure for random forests
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-119 |
| Pubmed ID | |
| Authors |
Silke Janitza, Carolin Strobl, Anne-Laure Boulesteix |
| Abstract |
The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. |
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 % |
|---|---|---|
| United States | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 2% |
| Germany | 2 | <1% |
| Canada | 2 | <1% |
| Belgium | 2 | <1% |
| Sweden | 1 | <1% |
| United Kingdom | 1 | <1% |
| Brazil | 1 | <1% |
| Malaysia | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 184 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 23% |
| Researcher | 43 | 21% |
| Student > Master | 27 | 13% |
| Student > Bachelor | 12 | 6% |
| Student > Doctoral Student | 10 | 5% |
| Other | 29 | 14% |
| Unknown | 33 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 36 | 18% |
| Agricultural and Biological Sciences | 34 | 17% |
| Environmental Science | 14 | 7% |
| Engineering | 12 | 6% |
| Mathematics | 11 | 5% |
| Other | 51 | 25% |
| Unknown | 43 | 21% |
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 22 April 2013.
All research outputs
#12,873,109
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#3,782
of 7,254 outputs
Outputs of similar age
#102,293
of 199,926 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 140 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 199,926 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 140 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.