A comparison of random forest variable selection methods for classification prediction modeling

Speiser, Jaime L.; Miller, Michael E.; Tooze, Janet A.; Ip, Edward H.

doi:10.1016/j.eswa.2019.05.028

Cited by 940 publications

(408 citation statements)

References 13 publications

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“…In this context, the use of Sentinel-2 data for habitat suitability mapping should be viewed as an effective compromise between spatial (10 m) and temporal resolution (5-6 days), as well as its open-data policy. Regarding the statistical methods inherent to SDMs, further studies are recommended in this research field in order to understand the best robustness of approaches able to handle high dimensional data [102], as well addressed to examine the predictive performances of multiple algorithms, especially when concomitantly integrated into an ensemble modeling framework [18,43]. This would be particularly interesting when evaluating how sub-sampled group of variables (remote-sensing products, topography, landscape variables) may singularly impact on the performance of species distribution models.…”

Section: Discussionmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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Accurate mapping is a main challenge for endangered small-sized terrestrial species. Freely available spatio-temporal data at high resolution from multispectral satellite offer excellent opportunities for improving predictive distribution models of such species based on fine-scale habitat features, thus making it easier to achieve comprehensive biodiversity conservation goals. However, there are still few examples showing the utility of remote-sensing-based products in mapping microhabitat suitability for small species of conservation concern. Here, we address this issue using Sentinel-2 sensor-derived habitat variables, used in combination with more commonly used explanatory variables (e.g., topography), to predict the distribution of the endangered Cabrera vole (Microtus cabrerae) in agrosilvopastorial systems. Based on vole surveys conducted in two different seasons over a~176,000 ha landscape in Southern Portugal, we assessed the significance of each predictor in explaining Cabrera vole occurrence using the Boruta algorithm, a novel Random forest variant for dealing with high dimensionality of explanatory variables. Overall, results showed a strong contribution of Sentinel-2-derived variables for predicting microhabitat suitability of Cabrera voles. In particular, we found that photosynthetic activity (NDI45), specific spectral signal (SWIR1), and landscape heterogeneity (Rao's Q) were good proxies of Cabrera voles' microhabitat, mostly during temporally greener and wetter conditions. In addition to remote-sensing-based variables, the presence of road verges was also an important driver of voles' distribution, highlighting their potential role as refuges and/or corridors. Overall, our study supports the use of remote-sensing data to predict microhabitat suitability for endangered small-sized species in marginal areas that potentially hold most of the biodiversity found in human-dominated landscapes. We believe our approach can be widely applied to other species, for which detailed habitat mapping over large spatial extents is difficult to obtain using traditional descriptors. This would certainly contribute to improving conservation planning, thereby contributing to global conservation efforts in landscapes that are managed for multiple purposes.

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Section: Discussionmentioning

confidence: 99%

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

et al. 2020

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“…UDC 004.93 DOI: 10.15587/1729-4061.2020.197319 models suggested in known literary sources [1][2][3][4][5][6][7][8][9][10][11][12][13][14], is associated with certain limitations and requirements for target functions. When applying such methods, it is impossible to increase the accuracy of the forecast when parameters change, for example, in forecasting the dependence of indicators of public health on pollutant emissions in the air.…”

Section: запропоновано генетичний метод для прогнозування показникIв mentioning

confidence: 99%

“…In [11], the results of studies into the use of Bayesian networks for the prediction of morbidity were reported. Bayesian networks have been shown to be an effective, compact, and intuitive way of representing the uncertainty-related knowledge.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Having analyzed works [10][11][12][13], we established that the set task can be resolved by the effective use of ANN because models based on artificial neural networks provide the possibility of processing multidimensional data of various types (thereby implementing the function of many variables), high adaptability to external changes, provide the possibility of synthesis of models with high approximating and generalizing properties. Therefore, it is necessary to develop a method for constructing neural network models based on empirical data, which would make it possible to synthesize the models of dependence of health indicators on the volumes of pollutant emissions.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The analysis of methods and tools of statistical prediction [7][8][9][10][11] indicates that the application of GA in a given aspect does not contradict the logic and mathematical basis laid down in these methods. In this regard, it is expedient to develop a forecasting model of the dependence of population health indicators on the volumes of emissions of pollutants in the air using GA and a modification of one of the operators of the genetic method [16].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Pancreatic ductal adenocarcinoma (PDAC) comprises two clinically relevant molecular subtypes that are currently determined using tissue biopsies, which are spatially biased and highly invasive. We used whole transcriptome sequencing of 10 plasma samples with tumor‐informed subtypes, complemented by proteomic analysis for minimally invasive identification of PDAC subtype markers. Data were validated in independent large cohorts and correlated with treatment response and patient outcome. Differential transcript abundance analyses revealed 32 subtype‐specific, protein‐coding cell‐free RNA (cfRNA) transcripts. The subtype specificity of these transcripts was validated in two independent tissue cohorts comprising 195 and 250 cases, respectively. Three disease‐relevant cfRNA‐defined subtype markers (DEGS1, KDELC1, and RPL23AP7) that consistently associated with basal‐like tumors across all cohorts were identified. In both tumor and liquid biopsies, the overexpression of these markers correlated with poor survival. Moreover, elevated levels of the identified markers were linked to a poor response to systemic therapy and early relapse in resected patients. Our data indicate clinical applicability of cfRNA markers in determining tumor subtypes and monitoring disease recurrence.

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A comparison of random forest variable selection methods for classification prediction modeling

Cited by 940 publications

References 13 publications

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Predicting Microhabitat Suitability for an Endangered Small Mammal Using Sentinel-2 Data

Construction of a genetic method to forecast the population health indicators based on neural network models

Minimally invasive determination of pancreatic ductal adenocarcinoma (PDAC) subtype by means of circulating cell‐free RNA

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