Gene selection with guided regularized random forest
Houtao Deng,George C. Runger +1 more
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TLDR
In this paper, an enhanced regularized random forest (RRF) is proposed, referred to as the guided RRF (GRRF), where the importance scores from an ordinary random forest are used to guide the feature selection process in RRF.About:
This article is published in Pattern Recognition.The article was published on 2013-12-01 and is currently open access. It has received 259 citations till now. The article focuses on the topics: Feature selection & Random forest.read more
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A random forest guided tour
TL;DR: The present article reviews the most recent theoretical and methodological developments for random forests, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures.
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A Random Forest Guided Tour
TL;DR: A review of the most recent theoretical and methodological developments for random forests can be found in this article, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures.
Journal ArticleDOI
A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.
TL;DR: Based on this study, the best variable selection methods for most datasets are Jiang's method and the method implemented in the VSURF R package, and for datasets with many predictors, the methods implement in the R packages varSelRF and Boruta are preferable due to computational efficiency.
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Flood hazard risk assessment model based on random forest
TL;DR: In this paper, an intelligent learning machine called Random Forest (RF) was used to solve the non-linear problems inherent to risk assessment, as well as estimating the importance degree of each index.
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A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
TL;DR: This work popularizes RF and their variants for the practicing water scientist, and discusses related concepts and techniques, which have received less attention from the water science and hydrologic communities.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Book
Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.