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Selection: with tag random-forests [6 articles] 

 

Statistical modeling: the two cultures (with comments and a rejoinder by the author)

  
Statistical Science, Vol. 16, No. 3. (August 2001), pp. 199-231, https://doi.org/10.1214/ss/1009213726

Abstract

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current problems. Algorithmic modeling, both in ...

 

Iterative random forests to discover predictive and stable high-order interactions

  
Proceedings of the National Academy of Sciences, Vol. 115, No. 8. (20 February 2018), pp. 1943-1948, https://doi.org/10.1073/pnas.1711236115

Abstract

[Significance] We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing. [Abstract] Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, ...

 

Classification and interaction in random forests

  
Proceedings of the National Academy of Sciences, Vol. 115, No. 8. (20 February 2018), pp. 1690-1692, https://doi.org/10.1073/pnas.1800256115

Abstract

Suppose you are a physician with a patient whose complaint could arise from multiple diseases. To attain a specific diagnosis, you might ask yourself a series of yes/no questions depending on observed features describing the patient, such as clinical test results and reported symptoms. As some questions rule out certain diagnoses early on, each answer determines which question you ask next. With about a dozen features and extensive medical knowledge, you could create a simple flow chart to connect and order ...

 

European Forest Types: toward an automated classification

  
Annals of Forest Science, Vol. 75, No. 1. (2018), pp. 1-14, https://doi.org/10.1007/s13595-017-0674-6

Abstract

[Key message] The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping. [Context] Forest Types classification systems are aimed at stratifying ...

 

Random forests

  
Machine Learning, Vol. 45, No. 1. (2001), pp. 5-32, https://doi.org/10.1023/a%3a1010933404324

Abstract

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 33

  
(February 2014)
Keywords: inrmm-list-of-tags   pyrus-tamamschianae   pyrus-theodorovii   pyrus-vallis-demonis   pyrus-voronovii   python   q-learning   qualitative-research   quantile-95   quantitive-variation   quantity-calculus   quantum-computing   quantum-gis   quarantine   quaternary   quercus-agrifolia   quercus-alnifolia   quercus-borealis   quercus-calliprinos   quercus-canariensis   quercus-castaneifolia   quercus-cerrioides   quercus-cerris   quercus-chrysolepis   quercus-coccifera   quercus-coccinea   quercus-crenata   quercus-dalechampii   quercus-douglasii   quercus-emoryi   quercus-engelmannii   quercus-faginea   quercus-falcata   quercus-frainetto   quercus-gambelii   quercus-garryana   quercus-hartwissiana   quercus-ilex   quercus-imbricaria   quercus-imeretina   quercus-kelloggii   quercus-lanuginosa   quercus-liaotungensis   quercus-lobata   quercus-macranthera   quercus-macrolepis   quercus-mirbeckii   quercus-mongolica   quercus-palustris   quercus-parvula   quercus-pedunculata   quercus-pedunculiflora   quercus-petraea   quercus-polycarpa   quercus-pontica   quercus-prinus   quercus-pubescens   quercus-pyrenaica   quercus-robur   quercus-rotundifolia   quercus-rubra   quercus-sessiflora   quercus-sessiliflora   quercus-sicula   quercus-spp   quercus-suber   quercus-trojana   quercus-variabilis   quercus-virgiliana   quercus-virginiana   quercus-wislizeni   quercus-x-morisii   radar   radial-growth   radiocarbon-chronology   rain-shadow   rainfall   rainfall-deciles   rainforest   rainy-days_daily-rainfall   random-forest   random-forests   random-walk   range-altitude   range-modelling   range-shift   rank-based-analysis   rapid-assessment   rare-events   rarely-observed-plant-species   rasterisation   ravine-forest   rcp26   rcp45   rcp60   rcp85   realised-vs-potential-range   realized-niche   reassuring-learning   receptivity  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/random-forests

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.