Intervention in prediction measure: a new approach to assessing variable importance for random forests
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Title
Intervention in prediction measure: a new approach to assessing variable importance for random forestsAuthor (s)
Date
2017-05Publisher
Springer Verlag; BioMed CentralBibliographic citation
Epifanio, I. BMC Bioinformatics (2017) 18: 230. doi:10.1186/s12859-017-1650-8Type
info:eu-repo/semantics/articlePublisher version
http://link.springer.com/article/10.1186/s12859-017-1650-8Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Background
Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. ... [+]
Background
Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction performance are not unique or there is not even a clear definition, as in the case of multivariate response random forests.
Methods
A new alternative importance measure, called Intervention in Prediction Measure, is investigated. It depends on the structure of the trees, without depending on performance measures. It is compared with other well-known variable importance measures in different contexts, such as a classification problem with variables of different types, another classification problem with correlated predictor variables, and problems with multivariate responses and predictors of different types.
Results
Several simulation studies are carried out, showing the new measure to be very competitive. In addition, it is applied in two well-known bioinformatics applications previously used in other papers. Improvements in performance are also provided for these applications by the use of this new measure.
Conclusions
This new measure is expressed as a percentage, which makes it attractive in terms of interpretability. It can be used with new observations. It can be defined globally, for each class (in a classification problem) and case-wise. It can easily be computed for any kind of response, including multivariate responses. Furthermore, it can be used with any algorithm employed to grow each individual tree. It can be used in place of (or in addition to) other variable importance measures. [-]
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BMC Bioinformatics (2017) 18:230Rights
© The Author(s) 2017
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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- MAT_Articles [766]
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