Dear Colleagues,

Random Forests was first proposed by Breiman (2001) and was subsequently developed by Breiman and Adele Cutler. Their code formed the basis of the Salford Systems (now Minitab) and R (Liaw and Wiener 2002) implementations of Random Forests. Due to its highly predictive accuracy for both classification and regression, without any tuning, its novel model selection algorithm, and its ability to perform both imputation and unsupervised learning, Random Forests has rapidly become one of the most popular and widely used machine/statistical learning methodologies. Random Forests is now implemented in most statistical packages and programming languages, including SAS and Python. Development and analysis of the algorithms that make up Random Forests continue with notable contributions, including Survival Random Forests (Ishwaran et al. 2008) and conditional variable importance for Random Forests (Strobl et al. 2008).

Random Forests has been applied to data in almost every area of research in which regression, survival analysis, and classification are used. One of the earliest published papers (Prasad et al. 2006) was an application of Random Forests to forest data. For this Special Issue, we are seeking papers in all areas of application of Random Forests to forest and remote sensing data, including comparison with other methods. We are particularly interested in novel uses of prediction, model selection, and visualization of relationships between response and predictor variables.

Prof. Richard Cutler
Guest Editor

References

1. Breiman, L. (2001). Random Forests. Machine Learning 45 5—32.
2. Iswaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer (2008). Survival Random Forests. Annals of Applied Statistics 4(3):841—860.
3. Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3):18—22.
4. Prasad, A. M., L. R. Iverson, and A. Liaw. (2006). Newer Classification and Regression Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9:181—199.
5. Strobl, C., A-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis. (2008). Conditional Variable Importance for Random Forests. BMC Informatics 9(307).

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Random Forests
• Machine/statistical learning
• Prediction
• Model selection
• Partial dependence