The basic principle of Random forest and its applications in Ecology— A case study of<i>Pinus yunnanensis</i>

Lei, 张雷 Zhang; Linlin, 王琳琳 Wang; Xudong, 张旭东 Zhang; Shirong, 刘世荣 Liu; Pengsen, 孙鹏森 Sun; Tongli, 王同立 Wang

doi:10.5846/stxb201306031292

Cited by 19 publications

(16 citation statements)

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“…A previous study used an RF model to predict the abundance of multiple tree species in Great Britain, and the accuracy of predictions reached high levels [41]. The large-scale distribution of Pinus yunnanensis was also predicted by RF, and prediction results were better than for other models [42]. Similar studies have been conducted for animals, by using RF to evaluate potential habitats of Syrmaticus reevesii, Manis pentadactyla, and Macana thibetana [43].…”

Section: Introductionmentioning

confidence: 75%

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

et al. 2020

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The potential distribution of the invasive plant Anredera cordifolia (Tenore) Steenis was predicted by Random Forest models under current and future climate-change pathways (i.e., RCP4.5 and RCP8.5 of 2050s and the 2070s). Pearson correlations were used to select variables; the prediction accuracy of the models was evaluated by using AUC, Kappa, and TSS. The results show that suitable future distribution areas are mainly in Southeast Asia, Eastern Oceania, a few parts of Eastern Africa, Southern North America, and Eastern South America. Temperature is the key climatic factor affecting the distribution of A. cordifolia. Important metrics include mean temperature of the coldest quarter (0.3 • C ≤ Bio11 ≤ 22.9 • C), max temperature of the warmest month (17.1 • C ≤ Bio5 ≤ 35.5 • C), temperature annual range (10.7 • C ≤ Bio7 ≤ 33 • C), annual mean air temperature (6.8 • C ≤ Bio1 ≤ 24.4 • C), and min temperature of coldest month (−2.8 • C ≤ Bio6 ≤ 17.2 • C). Only one precipitation index (Bio19) was important, precipitation of coldest quarter (7 mm ≤ Bio19 ≤ 631 mm). In addition, areas with strong human activities are most prone to invasion. This species is native to Brazil, but has been introduced in Asia, where it is widely planted and has escaped from cultivation. Under the future climate scenarios, suitable habitat areas of A. cordifolia will expand to higher latitudes. This study can provide a reference for the rational management and control of A. cordifolia.

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Section: Introductionmentioning

confidence: 75%

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

et al. 2020

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“…The PLS regression method was an extension of the multiple linear-regression model, which was widely used because it could reduce the problem of collinearity between data variables [10,11,12]. Furthermore, although the random forest (RF) model, which was used mostly in biology and had high predictive and learning ability, resolved the problem of singular values between response variables and explanatory variables [13], few reports had used it to monitor the nitrogen content in corn. Thus, the present study applied these three categories of spectral variables based on extracted hyperspectral information and combines them with sensitive variables selected by using the successive projections algorithm (SPA) to estimate the LNC of corn.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables

Fan

Zhao

Xu³

et al. 2019

Sensors

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Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC.

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“…(Zhang et al, 2014) In broad-spectrum, the classification or regression function depends on many predictors. Partial dependence of classification or regression function for a particular variable (X j ) defined as function of exception of remaining variables.…”

Section: Ballpark Matrix Of Datamentioning

confidence: 99%

Financial Fraud Detection Model: Based on Random Forest

Li¹,

Chan²,

Kazmi³

et al. 2015

IJEF

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Business's accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables' importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the most important variable in the model. Moreover, we applied four statistic methodologies, including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher accuracy than non-parametric models. However, Random Forest can improve the detection efficiency significantly and have an important practical implication.

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The basic principle of Random forest and its applications in Ecology— A case study ofPinus yunnanensis

Cited by 19 publications

References 0 publications

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models

Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables

Financial Fraud Detection Model: Based on Random Forest

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