Regression trees for modeling geochemical data—An application to Late Jurassic carbonates (Ammonitico Rosso)

Coimbra, Rute; Rodríguez-Galiano, Víctor; Olóriz, Federico; Chica-Olmo, Mario

doi:10.1016/j.cageo.2014.09.007

Cited by 20 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to constructing each tree using a different bootstrap sample of the data, RFs change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables while in an RF, each node is split using the best among a subset of predictors randomly chosen at that node [53]. This somewhat counterintuitive strategy turns out to perform very well compared with many other classifiers, including discriminant analysis, SVMs and NNs, and is robust against overfitting [4] [10].…”

Section: Random Forestmentioning

confidence: 99%

“…A main advantage of SVM classification is that it performs well on datasets that have many attributes, even when there are only a few cases that are available for the training process [7]. However, several disadvantages of SVM classification include limitations in speed and size during both training and testing phase of the algorithm and the selection of the kernel function parameters [53].…”

Section: Attributes Of the Classification Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Boateng¹,

Otoo²,

Abaye³

2020

JDAIP

165

View full text Add to dashboard Cite

In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows,

show abstract

Section: Random Forestmentioning

confidence: 99%

Section: Attributes Of the Classification Algorithmsmentioning

confidence: 99%

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Boateng¹,

Otoo²,

Abaye³

2020

JDAIP

165

View full text Add to dashboard Cite

show abstract

“…Support vector machine (SVM) classification methods do have some problems, though. The training and testing phases of the algorithm are not very good, and there are limits on speed and size when choosing kernel function parameters [25]. Random forests are a new and beneficial classification method that only needs two parameters to be set when making a predictive model: the number of decision trees that are formed (t) and the number of input features that are taken into account when each node of the decision tree is split up (m) [26].…”

Section: Introductionmentioning

confidence: 99%

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Luan,

Shen,

et al. 2023

Sustainability

View full text Add to dashboard Cite

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.

show abstract

Spatial Analysis and Simulation

Pei

Gong

et al. 2016

Springer Geography

View full text Add to dashboard Cite

Regression trees for modeling geochemical data—An application to Late Jurassic carbonates (Ammonitico Rosso)

Cited by 20 publications

References 50 publications

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review

Research on Maize Acreage Extraction and Growth Monitoring Based on a Machine Learning Algorithm and Multi-Source Remote Sensing Data

Spatial Analysis and Simulation

Contact Info

Product

Resources

About